A method for determining biomarker(s) pertaining to mental health disorder(s) and biomarker(s) determined therefrom

Abstract

The present disclosure provides a method for biomarker analysis. More particularly, provided herein is a method for identifying biomarker(s) associated with mental health disorder(s) such as but not limited to Major Depressive Disorder (MDD). The said method of the present disclosure is based on an integrative analysis between machine learning and bioinformatics techniques. The method allows efficient mining of publicly available datasets pertaining to mental health disorder patients to identify biomarkers that can aid in the diagnosis and prediction of the onset of such mental health disorders. Further envisaged herein are the biomarker(s) identified by the said method which allow early and efficient detection of mental health disorder(s) by non-invasive methods and application(s) of such biomarker(s).

Description

A Method for Determining Biomarker(s) Pertaining to Mental Health Disorder(s) and Biomarker(s) Determined TherefromTECHNICAL FIELD

[0001] The present disclosure provides a method for biomarker analysis. More particularly, the present disclosure provides a method for identifying biomarker(s) associated with mental health disorder(s) such as but not limited to Major Depressive Disorder (MDD). The identified biomarkers allows early and efficient detection of mental health disorder(s) by non-invasive methods.BACKGROUND

[0002] Major Depressive Disorder (MDD) is among the most prevalent, chronic complex psychiatric disorders nowadays. Depression affects over 280 million people globally according to the World Health Organization (WHO, 2023), and is the second leading cause of disability worldwide after cancer, while being predicted to be the leading cause by 2030. In the post- COVID-19 era, mental and behavioral disorders are reported to have become more severe, possibly due to the pandemic effects on healthcare and the economy worldwide.

[0003] MDD is a heterogeneous disorder, resulting from a complex interaction of social, psychological, environmental and genetic factors. Depression does not only affect the mental and psychological aspects of the individual’s health, but also affects physical health by disturbing the heart, kidney, nervous system, and immune system. It is characterized by the presence of depressed moods, functional impairments, a loss of interest in activities, fatigue, sleep disturbances, and psychomotor retardation or agitation. This negatively affects the patient’s productivity, self-perception, and self-esteem, resulting in an impaired quality of life which can lead to suicidal ideation and attempt. The WHO reported that more than 700,000 individuals worldwide die as a result of suicide each year, with depression being a leading cause (2023). Thus, MDD has grown into a major public health problem that needs urgent attention.

[0004] Despite extensive research, the pathophysiology of MDD is still poorly understood. Around 40% of MDD patients do not show an adequate response or remission to anti- depressant treatment and ultimately develop treatment resistance which further exacerbates the disease. Additionally, the diagnosis of MDD continues to be made on clinical assessment,which can be assisted by psychiatric questionnaire-based tools rather than laboratory -based tests which may be associated with several limitations and heterogeneity of the illness. Indeed, self-administered questionnaire -based approaches are not sufficient in evaluating efficiently the differences across patients’ sub-groups and identifying patients’ depressive stages. Adding to the fact that the absence of precise diagnostic biomarkers has resulted in a complex MDD diagnosis with other etiologically associated disorders such as bipolar disorder.

[0005] The use of functional neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) offer potential means for the assessment of the changes in brain activity associated with MDD as well as the prediction of response to treatment. However, these techniques are yet to be utilized in clinical practice due to multiple factors, including impracticality, expensive costs and limited specificity. Therefore, it is of great importance to screen for sensitive and specific biomarkers to improve MDD diagnostics and treatment.

[0006] Studies have shown that MDD affects the functional activity of the brain which can be identified by analyzing data on aberrations in the neuronal network. Moreover, the heterogeneity of depressive disorders may also be related to neuronal plasticity correlated with different depressive symptomatology. Besides, it is suggested that MDD results from systemic changes in the signaling and biochemical pathways involved in the regulation of moods, cognitive functions and disposition. Therefore, a comprehensive understanding of affected or associated biological pathways involved in MDD is of high importance to reveal the molecular mechanism of MDD and to identify accurate targets for MDD diagnosis.

[0007] To date, machine learning (ML) as an application of Al approaches is successfully applied in medical studies, by training computer models to process and understand complex patterns, for example within big-data, which facilitate classifications or predictions of new cases. Moreover, using OMICs strategies is amongst the most applied approaches at the forefront of personalized medicine in psychiatry with high efficiency, such as transcriptomics which showed increasing evidence of the potential to detect, and substantially improve the treatment of such complex cognitive disorders. Furthermore, there is a rising interest in using integrative OMICs and neuroimaging data to understand how an altered genetic profile in psychiatric disorders could influence brain structure and function. This can provide a deeper understanding of molecular mechanisms and uncover endophenotypes -neurobiological changes because of genes implicated in depression.

[0008] To better understand the mental health conditions and provide better patient care, early detection of mental health problems is an essential step.

[0009] The present disclosure sets out to compare transcriptomics data of MDD patients to healthy controls using an integrative approach reliant on bioinformatics and ML based techniques, to identify key biomarkers that can aid in the early diagnosis and prediction of the onset of depression in patients.SUMMARY

[0010] Addressing the above identified need in the art, the present disclosure provides a method for identifying biomarker(s) for detection of a mental health disorder, the method comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model is implemented to create an environment such that a learning agent, associated with the environment, is configured to: determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; and identify, based on the similarity score, a first set of biomarker(s) indicative of the mental health disorder(s); and the bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarker(s) indicative of the mental health disorder(s); anddetermining, based on an overlap between the first set of biomarker(s) and the second set of biomarker(s), the biomarker(s) for detection of the mental health disorder(s).

[0011] In some embodiments, implementing the biomarker identification model comprises applying a machine learning (ML) model, based on the preprocessed dataset, to create an environment, wherein the learning agent is associated with the environment, the learning agent being configured to: determine patterns and relationships among the one or more input features that are indicative of the mental health disorder(s); computing a similarity score of the determined patterns and relationships based on related contribution to the detection of the mental health disorder(s); and identifying, based on the associated similarity scores, one or more target input features that are indicative of the mental health disorder(s).

[0012] In some embodiments, the biomarker identification model comprises two or more machine learning (ML) models, to create the environment, wherein the learning agent is associated with the environment, the learning agent being configured to: select genes altered by the mental health disorder(s) based on individual outputs of the two or more ML models; perform hyperparameter optimization for the individual outputs of the two or more ML models; perform cross-validation on the individual outputs of the two or more ML models; identifying, based on the cross-validation, the first set of biomarker(s) that are indicative of the mental health disorder.

[0013] In some embodiments, the aforesaid method comprises: implementing a training agent in the environment of the biomarker identification model, wherein the training agent is configured to train the learning agent using information of the set of healthy reference parameters associated with the one or more healthy controls, and the set of diseased reference parameters associated with the one or more diseased subjects from the repository dataset.

[0014] In some embodiments, the similarity score of the biomarker(s) is determined by:identifying patterns and relationships among the one or more input features that are indicative of the mental health disorder.

[0015] In some embodiments, the dataset comprising biological and clinical data, wherein the biological data includes genetic, proteomic, metabolic, and neuroimaging information, and the clinical data includes behavioral and psychological parameters.

[0016] In some embodiments, the dataset may be specific to the mental health disorder(s) for which the biomarker(s) is to be determined.

[0017] In some embodiments, preprocessing of the dataset comprises: performing data analysis on the dataset according to a data normalization rule to obtain normalized and regularized dataset, wherein the data normalization rule comprises a data classification rule and one or more data filtering conditions.

[0018] In some embodiments, the mental health disorder(s) is selected from a group comprising major depressive disorder (MDD), anxiety, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and bipolar disorder.

[0019] In some embodiments, the dataset further comprises contextual features, wherein the contextual features are associated with one or more biological parameters and clinical parameters related to the mental health disorder(s).

[0020] In some embodiments, the above method further comprises: generating a mapping profile of the determined biomarkers with corresponding brain regions and structural alterations thereof; associating the mapping profile with cognitive functions and varied range of behaviors.

[0021] In some embodiments, the mental health disorder is MDD and wherein determined biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, EIP, LCN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 or any combination thereof.

[0022] Further provided herein is a method for determining level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1, in a biological sample obtained from a subject.

[0023] In some embodiments, the biological sample is a liquid biopsy sample. In some embodiments, the liquid biopsy sample is selected from a group comprising blood sample, saliva sample,

[0024] In some embodiments, the level of biomarker(s) is determined by PCR.

[0025] In some embodiments, further envisaged herein is the method for determining the level of biomarker(s) in a biological sample as described above, for use in one or more of: a) diagnosing MDD; b) diagnosing and treating MDD; c) determining genetic predisposition to MDD; and d) monitoring response to MDD therapy in patients diagnosed with MDD.

[0026] Further envisaged herein is a method for diagnosing mental health disorder(s), the said method comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method as described above.

[0027] In some embodiments, the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, EIP, LCN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBSE

[0028] Further provided herein is a method of diagnosing and treating mental health disorder(s) in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) identified by the method as described above, compared to a healthy control;- diagnosing the subject with the mental health disorder(s) once differential expression level of the one or more biomarker(s) is confirmed; and- administering therapy for the mental health disorder(s) to the diagnosed subject.

[0029] In some embodiments, the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBSE

[0030] In some embodiments, the therapy includes administration of Venlafaxine to the subject.

[0031] Further provided herein is a method for identifying genetic predisposition of a subject to mental health disorder(s) comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method as described above.

[0032] Further envisaged herein is a kit for determining differential expression level of one or more biomarker(s) identified by the method as described above in a biological sample, for detection of mental health disorder(s), comprising at least one amplification primer and / or at least one probe that hybridizes to nucleotide(s) encoding the one or more biomarker(s).

[0033] Further provided herein is a kit for determining differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, EIP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 in a biological sample, comprising at least one amplification primer and / or at least one probe that hybridizes to nucleotide(s) encoding the one or more biomarker(s).

[0034] In some embodiments, in the above-described kit, the amplification primers for NRG1 gene expression evaluation comprise Forward:5'-TCGTGGAATCAAACGCTACA-3' and Reverse:5'-ACTCCCCTCCATTCACACA-3'.

[0035] Also provided herein is use of biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 or any combination thereof, for the diagnosis of MDD.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[0036] The novel features and characteristics of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying figures. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

[0037] Figure 1 provides an overview of the bioinformatics and Machine Learning workflow.A) Data processing of MDD and healthy controls transcriptomic data. B) Bioinformatics analysis including GSEA. C) Machine Learning Pipeline.

[0038] Figure 2 depicts the top 20 enriched pathways in MDD patients compared to healthy controls.

[0039] Figure 3 depicts Venn diagram showing the overlap between the significantly identified genes by machine learning models and GSEA-leading gene analysis in MDD patients compared to healthy controls.

[0040] Figure 4: depicts network intersections of brain regions involving A) up-regulated andB) down-regulated genes profiled by the Allen Brain Atlas.

[0041] Figure 5 depicts gene expression proofing of the NRG1 gene in brain samples A) Subcortex, B) Left cortex from healthy samples.

[0042] Figure 6 depicts increased expression of NRG1 gene in MDD patients compared to Healthy controls. A) NRG1 was up-regulated in MDD patients compared to Healthy controls from the Microarray dataset. Y-axis is showing a random unit of expression. B) Confirmation of the NRG1 up-regulation in MDD patients compared to Healthy controls using RT-QPCR in an independent population. Y-axis is showing relative expression.

[0043] Figure 7 depicts the general workflow of MDD-related Biomarkers identification based on Bioinformatics and ML-approach to connect Transcriptomic changes and Brain regions.

[0001] It should be appreciated by those skilled in the art that any block diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.DETAILED DESCRIPTIONDefinitions

[0002] The phrase “mental health disorder” refers to health conditions characterized by a clinically significant disturbance in an individual's cognition, emotional regulation, or behaviour or a combination of these. This includes a wide range of conditions that affect mood, thinking and behaviour such as but not limited to clinical depression, anxiety disorder, bipolar disorder, Attention-deficit / hyperactivity disorder (ADHD), Schizophrenia, Obsessive compulsive disorder (OCD), Post traumatic stress disorder (PTSD) and the like.

[0003] The phrase “input features” or equivalent phrases bearing the same meaning in the context of the present disclosure are intended to refer to parameters and / or components of the dataset introduced into the integrative analysis engine of the present disclosure. The dataset comprising the input features may be subjected to preprocessing before being processed by the integrative analysis engine of the present disclosure. The preprocessing may involve one or more rounds of normalization and / or regularization.

[0004] The term “normalization” and equivalent terms refer to a data preprocessing technique employed to standardize the values of features in a dataset to a common scale, to transform the features to a similar scale and / or distribution. Normalization aids in improving the performance and accuracy of machine learning models by eliminating the potential biases and distortions caused by the different scales or distribution of features in the dataset.

[0005] As used herein, the term “regularization” and equivalent terms refer to a data preprocessing technique that prevents a model from overfitting which may occur when a model not only recognizes the inherent pattern within the training data but also incorporates the noise, potentially leading to subpar performance on fresh, unobserved data. Regularization may also include filtering to employ a set of regularity condition onto the features of the dataset.

[0006] The term “integrative analysis engine” refers to a module provided in the present disclosure that integrates functionalities of a bioinformatics model and a Machine Learning (ML)-based biomarker identification model to perform step(s) of the present subject matter.

[0007] As used herein, the term “bioinformatics analysis model” refers to a data analysis module reliant on one or more bioinformatics tools that predicts biomarkers indicative of mental health disorder by performing a comparative analysis to determine the anomaly between datasets pertaining to diseased and control subjects.

[0008] The term ‘biomarker identification model’ refers to a Machine Learning (ML) based model that is trained to determine the anomaly between datasets pertaining to diseased and control subjects and identify potential biomarkers that help in early diagnosis of mental health disorders or identification of genetic predisposition to mental health disorders.

[0009] As used herein, the term “artificial intelligence” or “Al” generally refers to machines or computers that can perform tasks in a manner that is “intelligent” or non -repetitive or rote or pre-programmed by mimicking cognitive functions and reasoning by incorporating various data perception and decision-making techniques.

[0010] As used herein, the term “trained” or “training” in the context of the ML model in the present disclosure refers to a process of providing input data points and corresponding target values to at least one model from which the at least one model examines and learn underlying patterns and relationships between the input data points and the corresponding target values. The ML model may perform said examination and learning by allocating certain scores or weights and bias to refine related predictive and / or deterministic processing in order to produce accurate responses to fresh or new input queries. Examples of models can be linear models, transformers, or neural networks such as convolutional neural networks (CNNs), Bayesian Neural Networks (BNN), and the like. In some embodiments, the model is trained on one or more of the data sets.

[0011] As used herein, the term “machine learning” refers to a type of learning in which the machine (e.g., computer program or engine) can employ supervised or unsupervised learning techniques based on assessment and / or computation being performed on an input dataset. Machine learning may utilize such techniques in order to perform required predictions and / or determinations.

[0012] As used herein, the term “neural net” refers to an interconnected network of computational nodes. The nodes are organized into a plurality of layers in which each layer comprises one or more nodes. The neural network is configured to receive an input and perform computation on the input in order to learn and solve complex relationships and identify patterns in the data associated with the input. Based on the computation, the neural network may generate a predictive and / or deterministic output.

[0013] The description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety ofoptional components are described to illustrate the wide variety of possible embodiments of the invention.

[0014] When a single device or article is described herein, it will be readily apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device / article may be used in place of the more than one device or article or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the invention need not include the device itself.

[0015] The term "about" is used herein to mean approximately, in the region of, roughly, or around. When the term "about" is used in conjunction with a numerical value / range, it modifies that value / range by extending the boundaries above and below the numerical value(s) set forth. As used herein, the term “about” a number refers to that number plus or minus 25%, plus or minus 15% or plus or minus 10 % of that number.

[0016] The term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0017] Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising” wherever used, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

[0018] With respect to the use of any plural and / or singular terms herein, those having skill in the art can translate from the plural to the singular and / or from the singular to the plural as is appropriate to the context and / or application. The various singular / plural permutations may be expressly set forth herein for sake of clarity.

[0019] As regards the embodiments characterized in this specification, it is intended that each embodiment be read independently as well as in combination with another embodiment. Forexample, in case of an embodiment 1 reciting 3 alternatives A, B and C, an embodiment 2 reciting 3 alternatives D, E and F and an embodiment 3 reciting 3 alternatives G, H and I, it is to be understood that the specification unambiguously discloses embodiments corresponding to combinations A, D, G; A, D, H; A, D, I; A, E, G; A, E, H; A, E, I; A, F, G; A, F, H; A, F, I;B, D, G; B, D, H; B, D, I; B, E, G; B, E, H; B, E, I; B, F, G; B, F, H; B, F, I; C, D, G; C, D, H;C, D, I; C, E, G; C, E, H; C, E, I; C, F, G; C, F, H; C, F, I, unless specifically mentioned otherwise.DisclosureMethod of biomarker determination

[0020] Addressing the need in the art with respect to determination of biomarker(s) suitable for early and efficient detection of mental health disorder(s), the present disclosure provides a bioinformatics and artificial intelligence (Al) reliant method, addressing the need to find means to mine publicly available datasets pertaining to mental health disorder patients to identify key biomarkers that can aid in the diagnosis and prediction of the onset of such mental health disorders. One such mental health disorder of particular significance, in the context of the present disclosure, is Major Depressive Disorder (MDD).

[0021] Particularly, the present disclosure provides a method reliant on integrative analysis between the bioinformatics and ML approaches to analyze and identify the most significant genes that display differential expression between datasets pertaining to subjects diagnosed with or predisposed to metal health disorder and healthy datasets.

[0022] More particularly, provided herein is a method for identifying biomarkers for detection of a mental health disorder, the method comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model is implemented to create an environment such that a learning agent, associated with the environment, is configured to:determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; and identify, based on the similarity score, a first set of biomarkers indicative of the mental health disorder; and the bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarkers indicative of the mental health disorder; and determining, based on an overlap between the first set of biomarkers and the second set of biomarkers, the biomarkers for detection of the mental health disorder.

[0023] Figure 1 provides a broad overview of the method of the present disclosure.

[0024] In some embodiments, the dataset is a publicly available dataset, preferably comprising information on biological parameters and clinical parameters of one or more subjects previously diagnosed with mental health disorder(s). The dataset, accordingly, may be selected based on the mental health disorder(s) for which the biomarker(s) is to be determined. For instance, if the method is to be relied upon for the detection of MDD, the data set may be a publicly available dataset, preferably comprising information on biological parameters and clinical parameters of one or more subjects previously diagnosed with MDD. In another example, if the method is to be relied upon for the detection of Schizophrenia, the data set may be a publicly available dataset, preferably comprising information on biological parameters and clinical parameters of one or more subjects previously diagnosed with Schizophrenia.

[0025] In some embodiments, the biological parameters include but are not limited to genetic information of subjects previously diagnosed with mental health disorder(s). A non-limiting example of such biological parameters includes but is not limited to genomic data, genetic data, transcriptomic data, proteomics data, metabolic data and neuroimaging data.

[0026] In a preferred embodiment, the biological parameters include transcriptomic data.

[0027] Accordingly, in an exemplary embodiment, the dataset comprises transcriptomic data of one or more subjects previously diagnosed with mental health disorder(s). In another exemplary embodiment, the dataset comprises transcriptomic data of multiple subjects previously diagnosed with mental health disorder(s) as derived from a publicly available database.

[0028] In some embodiments, the clinical parameters include but are not limited to information on cognition, emotional regulation and behaviour of subjects previously diagnosed with mental health disorder(s).

[0029] In a preferred embodiment, the clinical parameters include behavioural data. In another exemplary embodiment, the dataset comprises behavioural data of multiple subjects previously diagnosed with mental health disorder(s) as derived from a publicly available database.

[0030] In an exemplary embodiment, the dataset comprises behavioural data of subjects previously diagnosed with mental health disorder(s).

[0031] In some embodiments, preprocessing of the dataset comprises: performing data analysis on the dataset according to a data normalization rule to obtain normalized and regularized dataset, wherein the data normalization rule comprises a data classification rule and one or more data filtering conditions.

[0032] In some embodiments, obtaining the regularizing dataset may involve filtering the dataset. The filtering is selected from specific and / or adaptive filtering. In some embodiments, non-specific filtering may be carried out to extract the common variant probes, while adaptive filtering may be applied to identify probes with a predetermined cut off value for MAS 5 and GCRMA, for instance MAS5 value > 50 and a coefficient of variation > 10% in GCRMA. Then, processed probes may be intersected across the dataset.

[0033] In some embodiments, the preprocessing of the dataset to obtain a normalized and regularized dataset may be performed by one or more known method(s) and / or algorithm(s) such as but not limited to MAS5, PLIER, Li-Wong, RMA, GCRMA, justRMA and justGCRMA.

[0034] In a preferred embodiment, the preprocessing of the dataset is performed by MAS5 and / or GCRMA.

[0035] In some embodiments, implementing the biomarker identification model comprises applying a machine learning (ML) model, based on the preprocessed dataset, to create an environment, wherein the learning agent is associated with the environment, the learning agent being configured to: determine patterns and relationships among the one or more input features that are indicative of the mental health disorder; computing a similarity score of the determined patterns and relationships based on related contribution to the detection of the mental health disorder; and identifying, based on the associated similarity scores, one or more target input features that are indicative of the mental health disorder.

[0036] In some embodiments, the environment, created by the ML model, facilitates optimization for the ML model, and provides robust deployment capabilities for the ML model. The learning agent included in the environment is an intelligent agent which is deployed in the environment for efficiently training, optimizing, and deploy the ML model. In some embodiments, the learning agent is configured to initialize the ML model by selecting a suitable architecture for the ML model and continuous evaluation techniques to assess the model performance. Further, the learning agent fine-tunes the ML model based on the evaluation and deploys the ML model such that the ML model meets the required performance efficiency. Upon deployment, the learning agent may continuously monitor the model performance by adaptive feedback mechanisms and further optimize the ML model parameters to achieve the desired performance efficiency.

[0037] In some embodiments, implementing the biomarker identification model comprises applying a machine learning (ML) model, based on the preprocessed dataset, to create an environment, wherein the learning agent is associated with the environment, the learning agent being configured to: determine patterns and relationships among the one or more input features that are indicative of the mental health disorder and the healthy reference parameters associated with one or more healthy controls;computing a similarity score of the determined patterns and relationships based on related contribution to the detection of the mental health disorder; and identifying, based on the associated similarity scores, one or more target input features that are indicative of the mental health disorder.

[0038] In a non-limiting embodiment, the similarity score is computed on the basis of similarity to parameters of the diseased subject and / or deviation of the input parameters from parameters of the healthy control. For example, a high similarity score between an input parameter from parameters of the diseased subjects may be indicative of the mental health disorder. Alternatively, a low similarity score between an input parameter from parameters of the diseased subjects may be indicative of the input parameter being related to a healthy subject not suffering from the mental health disorder.

[0039] In some embodiments, the biomarker identification model comprises two or more machine learning (ML) models, to create the environment, wherein the learning agent is associated with the environment, the learning agent being configured to: select genes altered by the mental health disorder based on individual outputs of the two or more ML models; perform hyperparameter optimization for the individual outputs of the two or more ML models; perform cross-validation on the individual outputs of the two or more ML models; identifying, based on the cross-validation, the first set of biomarkers that are indicative of the mental health disorder(s).

[0040] In some embodiments, the hyperparameter optimization may be performed on the hyperparameters associated with the ML models. The term “hyperparameter” and equivalent terms refer to parameters associated to the ML model which control the behaviour and structure of the ML model. Examples of hyperparameters, may include, but are not limited to, a number of total layers in the network associated with the ML model, a regularization strength in some of the ML models, a learning rate associated with the ML model, and the like. The hyperparameter optimization may be performed for the individual outputs of the two or more ML models in order to determine a combination of hyperparameters that may result in the best performance of each of the ML models. Therefore, hyperparameter optimization is critical in achieving an optimal performance efficiency of the ML models.

[0041] In some embodiment, cross-validation may be performed on the individual outputs of the two or more ML models in order to assess an eligibility of individual output of each of the ML models for being generalized to an independent dataset. For example, the input parameters, provided to each of the ML models to obtained respective individual outputs, may be split into multiple subsets, for training the model in some of the subset (also referred hereinafter as training subset) and perform validation or evaluation the training subset by using the remaining subset (also referred hereinafter as validation subset). Additionally, cross-validation may be performed multiple times by selecting splitting the parameters for each ML model into different combination of the training subset and the validation subset. By performing the cross-validation by splitting parameters for each of the ML models into the training subset and the validation subset, the overall reliability of the individual outputs of the ML models. In addition, performing cross-validation for the individual outputs may prevent any potential problems, such as overfitting and underfitting, in the ML models. Further, cross-validation aids in determining optimal hyperparameters and selecting the best performing ML model(s).

[0042] In some embodiments, the two or more machine learning (ML) models are selected from but not limited to Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), Support Vector Machine Classifier (SVM), PCA, SelectKBest, and K-Nearest Neighbors (KNN).

[0043] In an exemplary embodiment, the two or more machine learning (ML) models are selected from a group comprising PCA, SelectKBest, LR, and RF.

[0044] In some embodiments, the hyperparameter optimization is performed on the basis of prediction performance calculated for each of the models. For example, in case it is identified that the prediction performance of a model does not fulfill a predefined performance criteria, the combination of the hyperparameters may be reconfigured or optimized until the prediction performance of the model fulfill the performance criteria.

[0045] In a non-limiting embodiment, the prediction performance of each classification model is estimated by accuracy, Fl score and ROC-AUC measures which are known in the art.

[0046] In some embodiments, the method comprises: implementing a training agent in the environment of the biomarker identification model, wherein the training agent is configured to train the learning agent using information of the set of healthy reference parameters associated with the one or more healthy controls, and the setof diseased reference parameters associated with the one or more diseased subjects from the repository dataset.

[0047] In some embodiments, the training agent is configured to receive data from the repository dataset. In some example embodiments, the training agent may be configured to receive and aggregate data from a plurality of data sources. The training agent may also be configured to perform normalization and regularization of the dataset and further segment / split the dataset into training subset and validation subset. In addition, the training agent may also segment the dataset into a test subset which may be employed for testing of the performance of the ML model.

[0048] In some embodiments, the bioinformatics analysis model may rely on any known bioinformatics techniques to compare the input dataset pertaining to subject suffering from or suspected to be suffering from a mental health disorder. The bioinformatics analysis model is a structured technique employed to determine the biomarkers indicative of mental health disorder by performing a comparative analysis to determine the anomaly between datasets pertaining to diseased and control subjects. The bioinformatics analysis model may be configured to receive one or more batches of data pertaining to diseased subjects and healthy controls. The bioinformatics analysis model employs gene enrichment techniques on the received batches and identify significantly enriched pathways indicative of the mental health disorder. Further, based on the identified enriched pathways, leading genes may be identified for each batch. Furthermore, the leading genes identified from each of the batches may be processed to obtain a subset of overlapping genes. Subsequently, the bioinformatics analysis model may filter the overlapping genes considering an upper threshold for upregulation and a lower threshold for upregulation, to obtain a subset of core genes. Therefore, based on the above approaches, the bioinformatics analysis model may identify the subset of core genes which are indicative of the mental health disorder.

[0049] In some embodiments, the bioinformatics analysis model may rely on a gene set enrichment analysis (GSEA) to reveal differentially expressed genes in mental health disorder patients and identify pathways they are associated with.

[0050] In some embodiments, when the mental health disorder is MDD, the bioinformatics analysis model identifies the activated and enriched cellular pathways in MDD patients in comparison to HCs. Without intending to be limited by theory, the bioinformatics analysismodel reveals that MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways.

[0051] The enrichment analyses of the DEGs in MDD patients are significantly related to the immune response and the inflammatory responses.

[0052] Without intending to be limited by theory, the aforesaid method of the present disclosure helps identify most significant and strong differential expression between data sets of subjects having mental health disorder(s) and healthy controls.

[0053] In some embodiments, the mental health disorder is selected from but not limited to major depressive disorder (MDD), anxiety, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and bipolar disorder.

[0054] In a preferred embodiment, the mental health disorder is MDD.

[0055] Thus, in another preferred embodiment, the present disclosure provides application of an integrative analysis based method reliant on a mixture of bioinformatics and ML approaches to mine transcriptomics data of patients with MDD compared to HCs.

[0056] Accordingly, in some embodiments, envisaged herein is a method for identifying biomarker(s) for detection of MDD, the method comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model is implemented to create an environment such that a learning agent, associated with the environment, is configured to: determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; andidentify, based on the similarity score, a first set of biomarkers indicative of MDD; and the bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarkers indicative of MDD; and determining, based on an overlap between the first set of biomarkers and the second set of biomarkers, the biomarkers for detection of MDD.

[0057] Without intending to be limited by theory, the identification of such biomarkers associated with MDD would provide a better and earlier preventative intervention, and insight into patients' responses to anti-depressant therapy, further aiding in monitoring the effectiveness of treatment in patients. The identification of specific depression-related biomarkers is crucial to aid in the diagnosis, prognosis, treatment and monitoring of patients with depression which may result in earlier diagnosis, as well as earlier intervention, and substantially improve the patients’ outcomes.

[0058] In some embodiments, the dataset further comprises contextual features, wherein the contextual features are associated with one or more biological parameters and clinical parameters related to the mental health disorder(s). In a non-limiting example, the input features being pre-processed, prior to subjecting to the integrative analysis engine, may be further enriched with the contextual features. The contextual features may be obtained from disparate data sources. Said disparate data sources may include, but are not limited to, electronic health record (EHR) databases, Electronic Health Information Exchanges (HIE), public health data sources, and the like. Other suitable forms of historical data may also be employed as contextual features. Enriching the input features with the contextual features may further improve the accuracy of identifying biomarker(s) for detection of a mental health disorder.

[0059] In some embodiments, the contextual features may be selected from a group comprising data from a different population, data from a population before and after a natural calamity or event such as but not limited to a pandemic or an epidemic.

[0060] In some embodiments, when the dataset comprises contextual features, the above- described method of the present disclosure is equipped to provide a comparison between the contextual datasets and the other dataset(s) processed by the method of the present disclosure previously, simultaneously or subsequently.

[0061] In some embodiments, the biomarker identification model may include a classifier for predicting and classifying the input features into predefined classes or categories. Further, the biomarker identification model may implement an iteratively regenerating or refining of the classifier based on a determination that an output obtained by the classifier does not meet a predefined receiver operating characteristic (ROC) curve criteria. For example, if the output of the classifier is not meeting the predefined ROC curve criteria, the biomarker identification model may utilize a different subset of input parameters. Further, the biomarker identification model may allocate an associated weight to the output. Subsequently, if the classifier does not meet the predefined ROC curve criteria, the biomarker identification model may adjust or decrease the associated weight of the respective output until a generated output of the classifier meets the predefined ROC curve criteria.

[0062] In some embodiments, if the output of the classifier is not meeting the predefined ROC curve criteria, the biomarker identification model may utilize a different subset of input parameters along with a subset of contextual features until a generated output of the classifier meets the predefined ROC curve criteria.

[0063] Without intending to be limited by theory, the top identified mental health disorder related biomarkers are capable of modeling multivariate associations between brain plasticity and behavioural disorders.

[0064] Thus, in some embodiments, the aforesaid method of the present disclosure further comprises: generating a mapping profile of the determined biomarkers with corresponding brain regions and structural alterations thereof; associating the mapping profile with cognitive functions and varied range of behaviours.

[0065] Due to brain plasticity, not necessarily all mental-related biomarkers follow the usual computational analysis hypothesis. That is why applying the ML and computational approaches is of the highest importance to retrieve hidden key biological indicators.

[0066] In some embodiments, the mental health disorder is MDD, wherein determined biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 or any combination thereof. In some embodiments, when the mental health disorder is MDD, by mapping the identified MDD- related biomarkers across the Human brain regions, significant overlapped differentially expressed genes across the brain regions were found. The mapping network analysis showed close interconnections between the different involved frontal brain regions suggesting that the identified genes or biomarkers are significantly associated with cognitive functions which are probably altered in MDD.

[0067] Apart from the determination of biomarker(s) that may serve in the diagnosis of mental health di sorder(s), in some embodiments, the above-described method of the present di sclosure may be further employed for confirming reproducibility of expression changes of mental health disorder related biomarkers across different populations.

[0068] Accordingly, in some embodiments, further envisaged herein is a method for confirming reproducibility of expression changes of mental health disorder related biomarkers across different populations comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model being implemented to create an environment such that a learning agent, associated with the environment, is configured to: determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; and identify, based on the similarity score, a first set of biomarkers indicative of the mental health disorder; andthe bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarkers indicative of the mental health disorder; and determining, based on an overlap between the first set of biomarkers and the second set of biomarkers, the biomarkers for detection of the mental health disorder; and determining level of the biomarkers for detection of the mental health disorder in a biological sample obtained from a subject, wherein the step of determining the level of the biomarkers is optionally repeated on the same or different biological sample obtained from the same subject(s) or different subject(s); and wherein the determination of the biomarkers and the determination of the level of biomarkers is performed sequentially or at separate times.

[0069] In some embodiments, the above-described method of the present disclosure may be further employed for confirming reproducibility of expression changes of mental health disorder related biomarkers in the same population before and after a natural calamity or any event of potential evolutionary significance such as but not limited to a pandemic or an epidemic.

[0070] In some embodiments, the above-described method of the present disclosure as described herein generates a database containing or comprising input and / or output data.

[0071] In some embodiments, the method the present disclosure is versatile in its intended application and can be relied upon to determine or identify patterns and relationships between a plurality of biomarkers pertaining to diseased subjects associated with a mental health disorder (for example, MDD patients) and healthy controls. The determined or identified patterns and relationships may be indicative of one or more biomarker(s) associated with the mental health disorder.

[0072] In a non-limiting embodiment, examples of such applications include but are not limited to the use of the method as described above for early detection of a mental health disorder. Accordingly, further envisaged herein are devices, software, systems, and other methods that leverage the capabilities of artificial intelligence or machine learning techniques forbiomarker analysis as described above to make predictions with respect to biomarkers indicative of mental health disorder(s), such as but not limited to MDD.

[0073] Taken together, machine learning techniques employed in the above-described method enable the generation of models with increased deterministic and predictive abilities, and a combination of such method with bioinformatics methods increases the robustness and performance accuracy of the method of the present disclosure. Evidently, the present disclosure has a practical application and provides a technically advanced solution to the technical problems associated with existing techniques for identifying biomarker(s) for detection of a mental health disorder. The aforesaid technical advancements and practical applications of the disclosed method may be attributed to the aspect of efficiently implementing the bioinformatics analysis model and efficiently training the biomarker identification model to draw correlation biomarker(s) associated with diseased subjects and healthy controls and obtain the desired output pertaining to biomarker(s) characteristic of specific mental health disorder(s) by employing a suitable input dataset.Method of measuring the determined biomarker(s) in a biological sample

[0074] The present disclosure further envisages a method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject.

[0075] In some embodiments, the subject may be a subject suspected to be suffering from a mental health disorder. In an exemplary embodiment, the mental health disorder is MDD.

[0076] In some embodiments, the mental health disorder is MDD and the biomarker(s) determined by the method for identifying biomarker(s) for detection of MDD is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 or any combination thereof, in a biological sample obtained from a subject.

[0077] In a non-limiting embodiment, the method of determining level of one or more biomarker(s) as described above may include determination of the level any of the following biomarker(s) or combinations thereof as set out in Table A in the biological sample, wherein ‘X’ represents presence of the specific component of the combination -Table A

[0078] The above table is a depiction of individual biomarker(s) and combinations of biomarker(s) indicative of MDD or predisposition thereto. Each of the above combinations 1- 148 may thus be relied upon for the diagnosis of MDD or predict predisposition to MDD.

[0079] NRG1, coding for neuregulin 1, is a member of the epidermal growth factor (EGF) family of extracellular ligands that present an important role in the maintenance, development and reparation of both the central nervous system (CNS) and peripheral nervous system (PNS) through the ErbB signaling pathway. In some embodiments, NRG I is a particularly important biomarker for the detection of MDD. NRG1 is therefore an important liquid-biopsy biomarkerfor MDD diagnosis and prognosis. NRG1, further may serve as a predictive biomarker of the response to pharmacological antidepressant treatment since NRG1 changes expression profile in MDD patients compared to healthy control before and after therapy for MDD. In a non- limiting embodiment, the therapy for MDD comprises administration of anti-depressant drugs such as but not limiting to MDD.

[0080] In a non-limiting embodiment, the method for identifying biomarker(s) for detection the present disclosure particularly shows that an up-regulation of the NRG1 in MDD patients compared to HCs in liquid biopsy samples such as but not limited to blood and saliva samples results in deficits in the response to a stimulus, hyper-agitation and perturbation in the working memory.

[0081] Accordingly, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and one or more additional biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 0EFM4, SERPING1, TCN1, and THBS1.

[0082] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and two or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 0EFM4, SERPING1, TCN1, and THBS1.

[0083] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and three or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 0EFM4, SERPING1, TCN1, and THBS1.

[0084] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and four or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 0EFM4, SERPING1, TCN1, and THBS1.

[0085] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject comprises determining the level of biomarker NRG1 and five or more additional biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1.

[0086] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject comprises determining the level of biomarker NRG1 and six or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1.

[0087] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and seven or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1.

[0088] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and eight or more additional biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1.

[0089] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarkers CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1.

[0090] Reference to ‘one or more’ to ‘eight or more’ in the above embodiments envisages equal possibility of the additional biomarker(s) in combination with NRG1 being selected from any of CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 in any permutation and combination.

[0091] In some embodiments, the biological sample is a liquid biopsy sample.

[0092] In some embodiments, the liquid biopsy sample is selected from a group comprising blood sample, saliva sample

[0093] In some embodiments, the level of biomarker(s) in the biological sample is determined by technique(s) that allows quantitative gene expression evaluation of the biomarker(s) in the biological sample.

[0094] In some embodiments, the level of biomarker(s) in the biological sample is determined by techniques such as but not limited to PCR.

[0095] In some embodiments, the biological sample is analyzed for expression of biomarker(s) including NRG1 and the primers for NRG1 gene expression evaluation include Forward:5'- TCGTGGAATCAAACGCTACA-3' and Reverse:5'-ACTCCCCTCCATTCACACA-3'.

[0096] Accordingly, in some embodiments the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 in the biological sample, wherein the level of biomarker(s) is determined by PCR and the primers for NRG1 gene expression evaluation include Forwards '-TCGTGGAATCAAACGCTACA-3' and Reversed ACTCCCCTCCATTCACACA-3

[0097] In some embodiments, the method of determining level of one or more biomarker(s) determined by the aforesaid method for identifying biomarker(s) for detection of mental health disorder(s), in a biological sample obtained from a subject, comprises determining the level of biomarker NRG1 and one or more additional biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1, wherein the level of biomarker(s) is determined by PCR and the primers for NRG1 gene expression evaluation include Forwards '-TCGTGGAATCAAACGCTACA-3 ' and Reversed ACTCCCCTCCATTCACACA-3

[0098] In a non-limiting embodiment, the above method may find application in the diagnosis and monitoring of mental health disorder(s) such as MDD. Accordingly, in some embodiments, envisaged herein is the method as described above for use in one or more of: a) diagnosing mental health disorder(s); b) diagnosing and treating mental health disorder(s);c) determining genetic predisposition to mental health disorder(s); and d) monitoring response to MDD therapy in patients diagnosed with mental health disorder(s); wherein the mental health disorder(s) is associated with the biomarker(s) whose level is identified.

[0099] In some embodiments, envisaged herein is the method for determining the level of biomarkers CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 0EFM4, SERPING1, TCN1, and THBS1 in a biological sample for use in one or more of: a) diagnosing MDD; b) diagnosing and treating MDD; c) determining genetic predisposition to MDD; and d) monitoring response to MDD therapy in patients diagnosed with MDD.Method of Diagnosis

[0001] The present disclosure further provides a method for diagnosing mental health disorder(s), the said method comprising analyzing a biological sample from a subject to confirm presence or absence of the biomarker(s) identified by the above-mentioned method of the present disclosure for identification of biomarker(s) associated with the mental health disorder(s).

[0002] In some embodiments, the method for diagnosing mental health disorder(s) comprises analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above.

[0003] In some embodiments, the method for identifying biomarker(s) for detection of mental health disorder(s) provides a cut off value for relative gene expression level for the identified biomarker(s) vs. healthy control, wherein the cut off value for the biomarker(s) is correlated to a specific mental health disorder(s). Accordingly, in some embodiments, the method for diagnosing mental health disorder(s), comprises analyzing a biological sample from a subject to confirm if the expression level of the biomarker(s) associated with the mental health disorder(s) meets pre-determined cut off value for relative gene expression level for the said biomarker(s) vs. healthy control.

[0004] In some embodiments, the mental health disorder is MDD. In some embodiments, MDD is characterized by a differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0005] Accordingly, in some embodiments, the present disclosure provides a method for diagnosing MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0006] In some embodiments, the present disclosure provides a method for diagnosing MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0007] In some embodiments, the present disclosure provides a method for diagnosing MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of a single biomarker or any combination biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control, as set out in Table A.

[0008] In some embodiments, the present disclosure provides a method for diagnosing MDD, the said method comprising analyzing a biological sample from a subject to confirm if the expression level of the biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 meets a pre- determined cut off value for relative gene expression level for the said biomarker(s) vs. healthy control.

[0009] In some embodiments, the method of diagnosis is a non-invasive method.

[0010] Accordingly, in a non-limiting embodiment, the biological sample is a liquid biopsy sample.

[0011] In some embodiments, the liquid biopsy sample is selected from a group comprising blood sample, saliva sample.

[0012] In some embodiments, the diagnosis is performed by subjecting the biological sample to analysis by technique(s) that allow quantitative gene expression evaluation of the biomarker(s) in the biological sample.

[0013] In some embodiments, the diagnosis is performed by subjecting the biological sample to analysis by techniques such as but not limited to PCR.

[0014] In some embodiments, the biological sample is analyzed for differential expression of biomarker(s) including NRG1 and the primers for NRG1 gene expression evaluation include Forward:5 '-TCGTGGAATCAAACGCTACA-3 ' and Reversed '-ACTCCCCTCCA TTCACACA- 3'.Method of identifying genetic predisposition to a mental health disorder

[0015] The present disclosure further provides a method for identifying genetic predisposition of a subject to mental health disorder(s), the said method comprising analyzing a biological sample from a subject to confirm presence or absence of the biomarker(s) identified by the above-mentioned method of the present disclosure for identification of biomarker(s) associated with or known to play a role in genetic predisposition to the mental health disorder(s).

[0016] In some embodiments, the method for identifying genetic predisposition of a subject to mental health disorder(s) comprises analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above.

[0017] In some embodiments, the method for identifying biomarker(s) for detection of mental health disorder(s) provides a cut off value for relative gene expression level for the identified biomarker(s) vs. healthy control, wherein the cut off value for the biomarker(s) is correlated to genetic predisposition to a specific mental health disorder(s). Accordingly, in some embodiments, the method for identifying genetic predisposition of a subject to mental health disorder(s) comprises analyzing a biological sample from a subject to confirm if the expressionlevel of the biomarker(s) associated with the mental health disorder(s) meets pre-determined cut off value for relative gene expression level for the said biomarker(s) vs. healthy control.

[0018] In some embodiments, the mental health disorder is MDD. In some embodiments, as mentioned above, MDD is characterized by a differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, EIP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0019] Accordingly, in some embodiments, the present disclosure provides a method for identifying genetic predisposition of a subject to MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0020] In some embodiments, the present disclosure provides a method for identifying genetic predisposition of a subject to MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, 374 OLFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0021] In some embodiments, the present disclosure provides a method for identifying genetic predisposition of a subject to MDD, the said method comprising analyzing a biological sample from a subject to confirm differential expression level of a single biomarker or any combination biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, 374 OLFM4, SERPING1, TCN1, and THBS1 compared to a healthy control, as set out in Table A.

[0022] In some embodiments, the present disclosure provides a method for diagnosing MDD, the said method comprising analyzing a biological sample from a subject to confirm if the expression level of the biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 meets a pre- determined cut off value for relative gene expression level for the said biomarker(s) vs. healthy control.

[0023] In some embodiments, the method of identifying genetic predisposition of a subject to mental health disorder(s) is a non-invasive method. Accordingly, in a non-limitingembodiment, the biological sample is a liquid biopsy sample. In some embodiments, the liquid biopsy sample is selected from a group comprising blood sample, saliva sample.

[0024] In some embodiments, the identification of genetic predisposition to mental health disorder(s) is performed by subjecting the biological sample from a subject to analysis by technique(s) that allow quantitative gene expression evaluation of the biomarker(s) in the biological sample.

[0025] In some embodiments, the identification of genetic predisposition is performed by subjecting the biological sample to analysis by techniques such as but not limited to PCR.

[0026] In some embodiments, in the method of identifying genetic predisposition of a subject to MDD, the biological sample is analyzed for differential expression of biomarker(s) including NRG1 and the primers for NRG1 gene expression evaluation include Forward:5'- TCGTGGAATCAAACGCTACA-3' and Reverse:5'-ACTCCCCTCCATTCACACA-3'.Method of treating a mental disorder characterized by the biomarker(s) identified by the aforesaid method of the present disclosure

[0027] Further envisaged herein is a method of treating a subject expressing biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, comprising administering therapy for the mental health disorder(s) to the subject after being determined to express a differential level of one or more of the biomarker(s) associated with the mental health disorder(s), compared to a healthy control.

[0028] In some embodiments, provided in the present disclosure is a method of treating a subject expressing biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, comprising administering therapy for the mental health disorder(s) to the subject after being determined to express a differential level of one or more of the biomarker(s) associated with the mental health disorder(s), compared to a healthy control.

[0029] In some embodiments, the method comprises treating a subject expressing biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, comprising administering therapy for the mental health disorder(s) to thesubject after analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control.

[0030] In some embodiments, envisaged herein is a method of treating a subject diagnosed with a mental health disorder(s) by the method of diagnosis as described above, comprising administering therapy for the mental health disorder(s) to the subject after the diagnosis.

[0031] More particularly, in some embodiments, envisaged herein is a method of diagnosing and treating mental health disorder(s) in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, compared to a healthy control;- diagnosing the subject with the mental health disorder(s) once differential expression level of the one or more biomarker(s) is confirmed; and- administering therapy for the mental health disorder(s) to the diagnosed subject.

[0032] The therapy administered is suitable for treatment of the mental health disorder(s) characterized by differential level of the biomarker(s) as identified by the method for identifying biomarker(s) for detection of mental health disorder(s), wherein the said method provides a correlation between the biomarker(s) and differential level thereof vs. healthy control, and the mental health disorder(s).

[0033] In some embodiments, the method for identifying biomarker(s) for detection of mental health disorder(s) provides a cut off value for relative gene expression level for the identified biomarker(s) vs. healthy control, wherein the cut off value for the biomarker(s) is correlated to a specific mental health disorder(s). Accordingly, in some embodiments, the method of treating a patient expressing biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, comprises administering therapy for the mental health disorder(s) to the patient after a biological sample from the patient is determined to meet the pre-determined cut off value for relative gene expression level for the said biomarker(s) vs. healthy control.

[0034] In some embodiments, the mental health disorder is MDD. In some embodiments, as mentioned above, MDD is characterized by a differential level of one or more biomarker(s)selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 0LFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0035] Accordingly, in some embodiments, provided herein is a method of treating a patient for MDD comprising administering MDD therapy to a patient after a biological sample from the patient is determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0036] In some embodiments, the present disclosure provides a method of treating a patient for MDD comprising administering MDD therapy to a patient after being determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0037] In some embodiments, provided herein is a method of diagnosing and treating MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed; and- administering therapy for MDD to the diagnosed subject.

[0038] In some embodiments, provided herein is a method of diagnosing and treating MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of a single biomarker or any combination biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 as set out in Table A compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed; and administering therapy for MDD to the diagnosed subject.

[0039] In some embodiments, the present disclosure provides a method of treating a patient for MDD comprising administering MDD therapy to a patient after being determined to express differential level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0040] In some embodiments, provided herein is a method of diagnosing and treating MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed; and- administering therapy for MDD to the diagnosed subject.

[0041] In some embodiments, when the mental health disorder is MDD, the therapy may include administration of drugs suitable for the treatment of MDD.

[0042] In some embodiments, when the mental health disorder is MDD, the therapy may include administration of Venlafaxine to the patient.

[0043] Accordingly, in some embodiments, envisaged herein is a method of treating a patient for MDD comprising administering Venlafaxine to a patient after being determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0044] In some embodiments, further envisaged herein is a method of treating a patient for MDD comprising administering Venlafaxine to a patient after being determined to express differential level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0045] In some embodiments, provided herein is a method of diagnosing and treating MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed; and- administering Venlafaxine as therapy for MDD to the diagnosed subject.

[0046] In some embodiments, provided herein is a method of diagnosing and treating MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed; and- administering Venlafaxine as therapy for MDD to the diagnosed subject.Method of monitoring response to treatment for mental disorder (s) characterized by the biomarker(s) identified by the aforesaid method of the present disclosure

[0047] In some embodiments, the present disclosure further provides a method of monitoring response to treatment in a patient a) expressing biomarker(s) identified by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above and b) treated for the mental health disorder(s).

[0048] Further envisaged herein is a method of monitoring response to treatment in a patient a) diagnosed with a mental health disorder by the method of diagnosis as described above, and b) treated for the mental health disorder(s).

[0049] In some embodiments, the present disclosure provides a method of monitoring response to treatment for MDD in a patient wherein the treatment is provided after a biological sample from the patient is determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0050] In some embodiments, the present disclosure provides method of monitoring response to treatment for MDD in a patient wherein the treatment is provided after a biological sample from the patient is determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBSE

[0051] In some embodiments, the present disclosure provides a method of monitoring response to Venlafaxine as treatment for MDD in a patient wherein the treatment is provided after a biological sample from the patient is determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control.

[0052] In some embodiments, provided herein is a method of monitoring response to Venlafaxine as a treatment for MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, NRG1, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed;- administering Venlafaxine as therapy for MDD to the diagnosed subject; and- monitoring expression level of the biomarkers post treatment.

[0053] In some embodiments, provided herein is a method of monitoring response to Venlafaxine as a treatment for MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, NRG1, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed;- administering Venlafaxine as therapy for MDD to the diagnosed subject; and- monitoring expression level of the of a single biomarker or any combination biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1, as set out in Table A, post treatment.

[0054] In some embodiments, the present disclosure provides method of monitoring response to Venlafaxine as a treatment for MDD in a patient wherein the treatment is provided after a biological sample from the patient is determined to express differential level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, EIP, LCN2, 374 OEFM4, SERPING1, TCN1, and THBSE

[0055] In some embodiments, provided herein is a method of monitoring response to Venlafaxine as a treatment for MDD in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of biomarker NRG1 and one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, 374 OEFM4, SERPING1, TCN1, and THBS1 compared to a healthy control;- diagnosing the subject with MDD level of the one or more biomarker(s) is confirmed;- administering Venlafaxine as therapy for MDD to the diagnosed subject; and- monitoring expression level of the biomarkers post treatment.Application of the method(s) of the present disclosure and the biomarker(s) determined by the said methods

[0056] In some embodiments, envisaged herein is use of the method for identifying biomarker(s) for detection of mental health disorder(s) for providing a correlation between the biomarker(s), their expression level(s) in a biological sample and mental health disorder(s).

[0057] In some embodiments, further provided herein is use of the method for identifying biomarker(s) for detection of mental health disorder(s) for studying reproducibility of biomarker expression levels across different populations.

[0058] The present disclosure further provides use of the method for identifying biomarker(s) for detection of mental health disorder(s), for analyzing progression of mental health disorder(s) in subject(s).

[0059] Also provided herein is use of the method for identifying biomarker(s) for detection of mental health disorder(s), for analyzing response to treatment in subject(s).

[0060] The present disclosure also provides use of the method for identifying biomarker(s) for detection of mental health disorder(s), for analyzing the impact of environmental and / or social events on the expression of biomarker(s) characterizing mental health disorder(s).

[0061] Further envisaged in the present disclosure is use of the method of determining level of one or more biomarker(s) determined by the method for identifying biomarker(s) for detection of mental health disorder(s) as described above, in a biological sample obtained from a subject to diagnose or rule out mental health disorder(s).

[0062] The present disclosure further provides use of the biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 or any combination thereof, for the diagnosis of MDD.

[0063] Further provided herein is use of the biomarker NRG1 for the diagnosis of MDD.

[0064] Also envisaged herein is use of the biomarker NRG1 in combination with at least one biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 or any combination thereof, for the diagnosis of MDD.

[0065] The present disclosure also provides use of the biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 or any combination thereof, for preparing suitable probes / primers for incorporating in a point-of-care system or kit for the diagnosis of MDD.Biomarker panel / Kit

[0066] The present disclosure further provides a panel of biomarker(s) that can aid in the diagnosis and / or monitoring of patients with mental health disorder(s). The said biomarker(s) are those identified by the method for identifying biomarker(s) for detection of mental health disorder(s), as described above.

[0067] In an exemplary embodiment, the present disclosure provides a panel of biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 0LFM4, SERPING1, TCN1, and THBS1 that can aid in the diagnosis and monitoring of patients with MDD.

[0068] In some embodiments, provided herein is a panel of 10 biomarkers that can aid in the diagnosis and / or monitoring of patients with MDD, wherein the biomarkers are selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 or any combination thereof.

[0069] Amongst the above defined biomarkers, NRG1 exhibits higher expression in the amygdala and hippocampus brain subregions and is suggested as a particularly important non- invasive liquid-biopsy biomarker for the diagnosis of MDD patients. Accordingly, in some embodiments, provided herein is a panel of biomarkers that can aid in the diagnosis and / or monitoring of patients with MDD, wherein the biomarkers include NRG1 in combination with one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, 374 OLFM4, SERPING1, TCN1, and THBS1 or any combination thereof.

[0070] The present disclosure further provides a kit for determining differential expression level of one or more biomarker(s) determined by the method for identifying biomarker(s) for detection of mental health disorder(s), as described above, in a biological sample, for detection of mental health disorder(s), comprising at least one amplification primer and / or at least one probe that specifically hybridizes to nucleotide(s) encoding the one or more biomarker(s).

[0071] In some embodiments, the mental health disorder is MDD, and the biomarker(s) determined by the method for identifying biomarker(s) for detection of MDD is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 or any combination thereof. Accordingly, in some embodiments, provided herein is a kit for determining differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 in a biological sample, the said kit comprising at least one amplification primer and / or at least one probe that specifically hybridizes to nucleotide(s) encoding the one or more biomarker(s).

[0072] In some embodiments, the said kit finds application in the diagnosis and / or monitoring of MDD. Accordingly, in some embodiments, provided herein is a kit for diagnosis and / or monitoring of MDD comprising at least one amplification primer and / or at least one probe that specifically hybridizes to nucleotide(s) encoding one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4,SERPING1, TCN1, and THBS1 in a biological sample for determining differential level of expression of the biomarker(s) in the biological sample vs. healthy control.

[0073] In some embodiments, provided herein is a kit for diagnosis and / or monitoring of MDD comprising at least one amplification primer and / or at least one probe that specifically hybridizes to nucleotide(s) encoding one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 in a biological sample for determining differential level of expression of the biomarker(s) in the biological sample vs. healthy control, wherein the primers for NRG1 expression evaluation include Forward: 5'-TCGTGGAATCAAACGCTACA-3' and Reverse: 5'- ACTCCCCTCCATTCACACA-3'.

[0074] In some embodiments, the kit finds utility in diagnosis of one or more mental health disorder(s) such as but not limited to MDD.

[0075] In some embodiments, the kit finds utility in diagnosis of one or more mental health disorder(s) such as but not limited to MDD.

[0076] In some embodiments, the kit finds utility in analyzing progression of one or more mental health disorder(s) such as but not limited to MDD in subject(s).

[0077] In some embodiments, the kit finds utility in analyzing response to treatment in subject(s) being treated for mental health disorder(s) such as but not limited to MDD in subject(s).

[0078] In some embodiments, the kit finds utility in analyzing the impact of environmental and / or social events on the expression of biomarker(s) characterizing mental health disorder(s) such as but not limited to MDD.

[0079] In some embodiments, envisaged herein is a point-of-care system or kit for the diagnosis of MDD comprising at least one amplification primer and / or at least one probe that specifically hybridizes to the nucleotide(s) encoding one or more biomarker(s) selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, 374 OLFM4, SERPING1, TCN1, and THBS1 in a biological sample for determining differential level of expression of the biomarker(s) in the biological sample vs. healthy control,

[0080] In light of the technical advancements provided by the disclosed method and system, the claimed steps, as discussed above, are not routine, conventional, or well-known aspects in the art, as the claimed steps provide the aforesaid solutions to the technical problems existing in the conventional technologies. Further, the claimed steps clearly bring an improvement in determination of biomarker(s) that may be relied upon for accurate and early diagnosis of mental health disorder(s) such as but not limited to MDD.

[0081] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.EXAMPLES:

[0082] Materials and MethodsTranscrip tome MDD datasets

[0083] The Gene Expression Omnibus database (GEO) was searched for publicly available MDD and HCs transcriptomic datasets (Mips : / / w w w;ncbi .nlmmih .go y / gep / ) to choose the appropriate gene expression datasets. The selection criteria included studies for Homo sapiens and MDD patients recruited as per international practice guidelines in psychiatry. Studies with no healthy control samples or with a large batch effect were excluded. The GSE98793 dataset, which met all the inclusion criteria was selected and processed. The samples were performed in two batches, for which batch information was extracted from phenotypic data. This dataset consists of high-resolution gene expression sets retrieved from whole blood samples of 128 MDD patients and 64 healthy controls (HCs). The Affymetrix Human Genome U133-Plus 2.0 gene expression gene-chip was used. The MDD patients were diagnosed if at least two episodes of depression satisfying DSM-IV or ICD10 criteria were identified using the semi- structured Schedule for Clinical Assessment in Neuropsychiatry (SCAN). The demographic and clinical details of the GSE98793 dataset are listed in Supplementary Table SI.

[0084] The datasets GSE99725, GSE76826, GSE38206 and GSE32280 for confirmation external sets are available from Gene Expression Omnibus(https: / / www.ncbi.nlm.nih.gov / geo). The machine learning pipeline including sourcecode is uploaded to a publicly available repository at GitHub (https: / / github.com / maryjis / autoNeuro). Raw Microarray Normalization and Adaptive Filtering

[0085] The Affymetrix microarray covers more than 54,000 probes. The raw CEL files were downloaded and analyzed using an in-house R script pipeline. First, logarithmic transformation and batch effect correction were performed to eliminate the experimental effect on the datasets. Next, to normalize and filter out the background noise, MAS5 and GCRMA packages from the Bioconductor project (https: / / www.bioconductor.org / ) were applied. Non-specific filtering was carried out to extract the common variant probes, while adaptive filtering was applied to identify probes with MAS5 value > 50 and a coefficient of variation > 10% in GCRMA. Then, processed probes were intersected across all MDD patients and HCs to identify common variant probes. The filtered common probes between all samples from the two batches of the dataset were combined into a data matrix and mapped to the genes list using Broad Institute software (https: / / www.gsea-msigdb.org / gsea / index.jsp ). For the collapse of gene symbols, the probes with maximum expression were selected as the expression value for the gene. Probes mapping to housekeeping genes or not assigned to any gene were excluded. The workflow of the bioinformatics analysis is shown in Figure 1A. Gene Set Enrichment Analysis

[0086] The mapped gene expression list was thereafter subjected to the absolute Gene Set Enrichment Analysis (GSEA) to identify the activated and enriched cellular pathways in MDD patients in comparison to HCs Figure 1B. The absolute GSEA search was carried out on the expression data as previously described, using around 25,828 annotated cellular pathways obtained across seven well-annotated gene sets obtained from the Human Molecular Signatures Database (MSigDB). The significantly identified pathways were then subjected to the normal GSEA considering the significance cut-off of p-value p < 0.05. The significantly activated pathways were further processed to identify the differentially enriched genes between the MDD patients and HCs. As parameters, random permutations=1000, permutation type=sample labels and enrichment correlation-based weighting=1 were used. Top leading gene analysis was applied for all significant gene sets to identify the core leading genes for each pathway. Only genes, with fold change (FC)>1.5 considered as upregulated and FC<0.5 considered as downregulated, were further explored in the downstream analysis.Machine Learning pipeline

[0087] ML analysis was performed on the same mapped gene expression list from the GSE98793 dataset using SkLeam v.1.2.1 library in Python v3.9. To examine various ML models with different feature selection (FS) approaches a common ML pipeline was applied. The ML pipeline selection involves FS and hyperparameter optimization for different ML models including Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), Support Vector Machine Classifier (SVM) and K-Nearest Neighbors (KNN) with 10- fold cross- validation (Figure 1C). The prediction performance of each classification model was estimated by accuracy, Fl score and ROC-AUC measures. The accuracy is the proportion of correctly predicted subjects among all subjects. Its formula is presented below:where TP = True Positive; FP = False Positive; TN = True Negative; FN = False Negative

[0088] The Fl score is the harmonic mean of precision and recall. The precision is the fraction of truly predicted subjects related to a certain class among all subjects for which a classification model was assigned to this class. The recall is the fraction of truly predicted subjects related to a certain class among all subjects related to this class. The Fl-macro score was used, where all per-class Fl scores were averaged.

[0089] ROC-AUC is the area under the error curve, which evaluates the quality of the model without being tied to a specific threshold. For building the ROC curve, TPR against the FPR is plotted, and then the area under the resulting curve was calculated.Features selection using ML

[0090] Transcriptomic data consists of a large number of genes (features) that are much more than the number of samples, which leads to model overfitting in the classification tasks. The FS procedure is applied to reduce the dimensionality of the data while preserving significant information. Considering that the number of features should be equal to or smaller than the sample size, thus, the following options were chosen: First, the number of selected features was equal to the number of samples in the dataset, while the second option, the number of selected features was 80% of the number of samples. Several methods in the FS procedure (Figure 1C) were chosen including PCA and the SelectKBest method with ANOVA F-value as a score function. Moreover, ML methods such as LR and RF were chosen to extract important features from trained models. The methods of important feature extraction are different for LR and RF. In the LR model, absolute weights for each feature were ranged in descending order and the top N of them (N is chosen according to the options discussed above) were selected for the next step. RF is a method that fits several decision tree classifiers on various splits of the dataset. The importance of each feature was calculated in several steps: First, the value of the decrease in impurity criterion for each node associated with the feature in each decision tree is calculated, then the sum of calculated values of decrease in impurity criterion on all nodes is multiplied by the percentage of examples reaching these nodes. This weighted sum is a feature of importance in the RF model.Machine Learning methods

[0091] Several ML models were trained as shown in Figure l.C to distinguish between MDD patients and HCs based on selected features only. Different FS methods and parameter combinations inside the ML pipeline selection block were applied including the GridSearchCV procedure (skleam Python library, version 0.24.2) for fine-tuning the models. For estimation of the model's performance, 10-fold cross-validation with Fl metric was used due to class imbalance. Fl -measure on cross-validation for medical datasets was calculated as previously described. The total number of true positives and false positives over the folds was estimated and then F-measure was computed on them. Similarly, the ROC-AUC and Fl scores were computed. The best-performing models optimized in the pipeline were compared with the baseline model. LR with default parameters and 12 regularization was selected as a baseline.Finally, the top 20 important features were extracted from each best-performing model (on each batch) and each baseline model (for batchl and batch!) on 10 repeated 10-fold cross- validation, forming a list of top potential biomarkers between MDD patients and HCs groups.Evaluation Measures of the Classification Performance

[0092] In order to provide a performance assessment of the best-built classifiers that were trained on the merged (batch 1 and batch 2) discovery dataset, evaluation measures of the classification performance were performed in independent external transcriptomics datasets. Four well-characterized independent datasets from different populations were retrieved from the Gene Expression Omnibus database (GEO). MDD diagnosis was performed per the DSM- IV criteria . The total cohort of 42 MDD patients and 57 HCs among the four external datasets are detailed in Table 1.Table 1: List of Transcriptomic datasets used in this study.

[0093] First, evaluated the best-built classifiers were evaluated, which consist of the LR best model along with LR as the feature selection method, in the four confirmation datasets. Next, the best-built classifiers were compared with the baseline classifiers' performance to calculate the relevant performance metrics of our model built compared to the baseline Fl -macro in each dataset. A dummy classifier was built that predicts the most frequent class label. The Fl -macro score for the dummy classifier was calculated using the following formula: r / (r + 1), where r is the probability of the most frequent class. Since the confirmation datasets showed different gene sets compared to the discovery dataset, subsequently, the best classifiers were trained again using the discovery dataset after removing the genes that were absent in the confirmation dataset. Moreover, to improve the quality of the classification, transfer learning was applied in each external dataset by splitting the dataset into two parts; 80% from the new training dataset and 20% from the new test dataset.Functional mapping and annotation of biomarkers to Human brain regions

[0094] To evaluate the brain structure alterations in MDD patients associated with the identified biomarkers, functional mapping and annotation of the Human brain regions were performed using the Allen Human Brain Atlas (AHBA). The latter is a publicly available resource of comprehensive gene expression mapping of the human brain computing across 3700 spatially different tissues that are collected from six neurotypical postmortem adult brains.Occurrence of the identified biomarkers in a large cohort of MDD patients from diverse ethnicities

[0095] Cross-validation was performed for the potential DEGs between the MDD patients and HCs that were identified with bioinformatics analysis and ML models from the discovery dataset. Independent cross-validation sets were considered on four well-characterized independent datasets (GSE99725, GSE76826, GSE38206, and GSE32280) from different populations with diverse ethnicities (Caucasian, Japanese and Chinese) (Table 1). First, the Limma package under the R software was used to evaluate the DEGs between MDD patients and HCs in each external dataset at a significance threshold of p<0.05. Next, all described datasets including the discovery and the confirmation external datasets were combined and only the common genes were considered. ML was re -performed on 80% of the common genes among the entire datasets while the performance was evaluated in the 20% holdout.

[0096] Figure 7 depicts the general workflow of MDD-related Biomarkers identification based on Bioinformatics and ML-approach to connect Transcriptomic changes and Brain regions.Recruitment of MDD patients from the Kazakhstan population for biomarker assessment

[0097] The identified potential MDD biomarker was further assessed in an independent cohort with a particular genetic background from the Kazakhstan population. Saliva samples of 12 well-characterized MDD patients and 8 HCs were collected at Al-Farabi Kazakh National University, Kazakhstan. The study was approved by the Ethics Committee of the Al-Farabi Kazakh National University (IRB-AO83 and IRB-A267). Inclusion criteria were diagnosed with MDD by a psychiatrist for the first time (based on ICD-10) and no medication / treatment. Exclusion criteria were volunteers with substance or alcohol abuse and taking antipsychotic medications / treatment. All participants after signing the consent form completed the Inventory of Depressive Symptomatology (IDS). Subjects who showed IDS scores>20 were interviewed by a psychiatrist for diagnosis. Saliva samples were collected from all samples. mRNA gene expression evaluation using qRT-PCR

[0098] The top candidate biomarker NRG1 was selected for further quantitative gene expression evaluation between MDD patients and HCs from Kazakh population. Saliva samples were stored in RNAlater stabilization solution at +4 °C until analysis. Total RNA was extracted using the RNeasy Mini kit (Qiagen, Germany), according to the manufacturer’sprotocol. RNA purity and quantity were assessed using Nanodrop2000 (ThermoFisher Scientific, USA). Total RNA was transcribed using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, USA) according to the manufacturer’s instructions. The primers for NRG1 gene expression evaluation were Forward:5'- TCGTGGAATCAAACGCTACA-3' and Reverse:5'-ACTCCCCTCCATTCACACA-3'. 18S rRNA was used as a housekeeping gene with the primers Forward: 5'- TCGCTCCACCAACTAAGAAC-3' and Reverse: 5'-TGACTCAACACGGGAAAC-3'. The mRNA expression of the candidate and reference genes were quantified in MDD patients and HCs using Maxima SYBR Green / ROX qPCRMaster Mix (ThermoFisher Scientific) on the QuantStudio3 system (Applied Biosystems). The amplification for each sample was carried out in two biological replicates and two technical replicates, then the mean Cq value (quantitation cycle) was used to determine the fold expression change in MDD patients compared to the HCs. The relative expression levels of the target gene were calculated using the 2AACtmethod.ResultsIdentification of the differentially expressed genes among MDD patients

[0099] The differential transcriptomic profile across MDD patients and HCs were analyzed using a bioinformatics pipeline, and the result of data distribution before and after quality control, normalization and filtration of the GSE98793 dataset are shown in Figure 1A. Comparing MDD patients to HCs, a total of 3294 and 2088 differentially expressed genes (DEGs) were identified, respectively, in batch 1 and batch 2 subgroups. The total of DEGs was merged into a common expression matrix based on the gene names to include 1446 common genes between the 106 MDD patients and 51 HCs.Enrichment analyses of the DEGs in MDD patients

[0100] To explore the molecular signatures and biological functions and processes of the identified DEGs in MDD patients compared to the HCs, GSEA was performed. It consists of a computational method that identifies whether a priori set of genes shows statistically significant and coherent differences between two groups across several thousand pre-defined datasets including complex molecular mechanisms, immunologic signatures, biological processes, molecular activities and cellular structures. The enrichment analysis showed that a total of 3262 and 2186 significant pathways (p<0.05) were identified in batch 1 and batch 2,respectively. The GSEA revealed that the DEGs in MDD patients are mainly enriched in pathways related to immune response, immune effector processes, signaling by cytokines, inflammatory response, proinfl ammatory and profibrotic mediators, cellular responses to stimuli, neurodegeneration pathways, cerebellar atrophy pathway, and neuroactive ligand- receptor interactions. The details of all significant pathways are listed in Supplementary Table 2The most informative MDD-related genes from the activated cellular pathways

[0101] To identify the core genes that potentially determine the significant enrichment of the corresponding identified gene sets, the leading-edge analysis was also performed for all identified significant gene sets in each subgroup of MDD and HCs. Subsequently, the gene frequency was calculated based on the occurrence of a gene amongst all enriched leading-edge core genes from the significant over-represented gene sets in each batch. Overall, 353 frequent genes resulted based on the merged data of MDD patients compared to HCs (Supplementary Table 3). These identified genes could be considered the most informative genes that play or control potential biological roles among the over-enriched pathways in MDD compared to HCs. The functional annotation of these top leading genes in MDD patients further confinns that the immune response-related pathways, the inflammatory responses, and metabolic pathways are the main significantly enriched in MDD compared to HCs (Figure 2). Next, only genes with large gene expression FC>1.5 or <0.5 between MDD patients and HCs were considered. Hence, 28 significantly important genes between MDD patients and HCs were left as shown in Table 2.Table 2: Filtered Top leading genes in MDD patients compared to healthy controls.Explainable Machine learning models

[0102] To identify the most sensitive and potent predictive MDD-related genes, the above- resulted DEGs between MDD and HCs were further evaluated with another computational method by applying different ML models. The fine-tuned parameters for the best-built models in each subgroup comparing MDD patients to HCs were presented in Table 3.Table 3: Final ML model parameters.

[0103] Among all tested models, the best results were obtained with 3 final models based on the best-performing ML algorithms on separate and merged batches Table 4.Table 4: Final model's performance.

[0104] The best model obtained on the merged dataset was found to be LR with LR as the FS method (number of selected features =157). The final model’s score is 0.76±0.11 and ROC- AUC is 0.82+0.12. While, the best model in batchl is LR (Fl=0.73+0.17, ROC- AUC=0.88+0.13) and in batch2 is SVM (Fl=0.7+0.16, ROC-AUC=0.79+0.15). Additionally, the performance classification of the best-built classifiers trained on the merged discovery dataset which consists of the LR best model along with LR as the feature selection method was further evaluated in four well-characterized independent transcriptomics datasets from different populations. To compare the performance of the best model with baseline Fl -macro, built a dummy classifier was built that predicts the most frequent class label. Due to batch effect and domain shift, the Fl -macro score was similar to the baseline classifier in the majority of the datasets (Table 5). The batch effect appears due to differences in equipment settings and experiment design which leads to changes in the features’ distribution between training data and external confirmation datasets and thus results in poor classification performance. Hence, to resolve that, transfer learning was additionally applied to show that the classification performance in 10-fold cross-validation has improved for all datasets compared to the classifiers without pretraining, and in all cases, the Fl -macro score was better than the baseline as shown in Table 5.Table 5: The classification performance comparison (F1 -macro) with and without retraining compared to a baseline classifier in external transcriptomics datasets.Relevant and stable MDD features identified from the best-built ML models

[0105] For the discovery dataset, the performances of all models showed > 75% of accuracy for both joint and separate batches, consequently, it was decided to include features from the best models of the separate and merged data for the downstream analysis. From the best 3 identified models, the most informative and stable features were extracted as MDD-candidates biomarkers including the top 20 important features for batch 1, batch 2 and merged batches. The 20 important features from baseline models were also included for batch 1 and batch 2. The score for each feature was computed as a share of all folds, where this particular feature was selected by the model during the FS step. In this experiment, performed a total of 100 folds of cross-validation was performed with 10 runs of a 10-fold cross-validation procedure for each group of data to estimate the reliability of the obtained features. Next, after selecting the important features, post-hoc analysis (scipy Python library, version 1.9.1) was carried out to evaluate the significant differences in the gene expression between MDD patients and HCs. To assess the applicability of the T-test, the Shapiro test was applied to evaluate the normality distribution and Levene’s test was used to test the equality of variances, but if these assumptions were not met, the Mann- Whitney U-test was applied. For each comparison of theseparate and merged datasets of MDD patients compared to HCs, the significant important features are presented, respectively, in Supplementary Tables 4-6. Supplementary Table 4: The most informative and stable features (genes) in MDD patients compared to healthy controls (Batch 1). F S % l S i i lHNRNPM 50 00004 M Whi USupplementary Table 5: The most informative and stable features (genes) in MDD patients compared to healthy controls (batch 2).63Supplementary Table 6: The most informative and stable features (genes) in MDD patients compared to healthy controls (Batch 1 & Batch 2).HP 60 00002 Tt tMDD-related biomarkers emerged from Data Integration

[0106] The identified gene lists from ML final models and the top frequent genes from the GSEA on the merged data from batch 1 and batch 2 results were intersected to identify the MDD-related biomarkers. Since ML models are based on different principles and therefore may reveal different biomarkers, thus, the important features from the best three models were extracted to increase the set of overlapped biomarkers from the two approaches. The overlapping between the 86 most important features resulting from ML models and the 28 top leading genes resulting from the GSEA showed 10 common genes (Figure 3) including CEACAM8, CLEC12B, DEFA4, HP, LCN2, NRG1, OLFM4, SERPING1, TCN1, and THBS1. Mapping of the MDD-related biomarkers across the Human brain regions

[0107] To get a better understanding of the association between brain structural alterations and gene expression changes in MDD, the brain-wide gene expression changes of the identified MDD-related biomarkers was assessed, using the whole-brain transcriptomic dataset from the AHBA. The mapping of the 10 MDD-related biomarkers showed significant (p<0.05) related up-and down-regulated genes across the brain regions as listed in Table 6. Table 6: Significantly enriched brain regions profiled by Allen Human Brain Atlas of the MDD-related genes. B i i P MDD l d S i

[0108] The network analysis showed close interconnections between the different involved brain regions suggesting that the evaluated genes are biologically interconnected as a group in different cognitive functions and varied range of behaviors (Figure 4). Thus, the top identified MDD-related biomarkers are capable of modeling multivariate associations between brain plasticity and behavior disorders.Reproducibility of MDD-related biomarkers expression changes across different populations

[0109] To evaluate the identified MDD-related biomarkers in another cohort of 42 MDD patients and 57 HCs, independent external transcriptomic datasets were selected including different populations with diverse ethnicities (Caucasian, Japanese and Chinese). Transcriptomic analysis was performed for each dataset separately, then identification of DEGs was proceeded for each comparison of MDD patients compared to HCs and also for the follow- up of patients e.g. before / after surgery, remission and treatment. All studied comparisons weresummarized in Table 7. The 10 identified MDD-related biomarkers were evaluated across different comparisons to show that all these genes, except for the OLFM4 gene, were showing gene expression changes in at least one subgroup of MDD patients (Table 7). Table 7: Cross-Validation of the top MDD-related biomarkers across different populations of MDD patients and healthy controls. GSE C i G N f N f O l i DEGH lh C l MDD P i i 9 9 LCN2 ↑ SERPING1 ↑ THBS1

[0110] Importantly, NRG1, neuregulin 1, was the top frequently altered gene in three out of four different MDD populations. In the GSE99725 dataset, the NRG1 was up-regulated in HCs compared to MDD patients (FC=0.07, p=0.04) before and after (FC= 0.11, p=0.03) surgery, however, it didn’t show a significant variation (p>0.05) once comparing MDD patients before and after surgery. Likewise, in the GSE76826 dataset, the NRG1 was down-regulated once comparing HCs to MDD patients before remission and after remission (FC=-0.87, p=0.0007) and (FC=-0.85, p=0.0005), respectively, while no gene expression changes were detected once comparing MDD patients before and after remission. Furthermore, in the case of Venlafaxine treatment in the GSE32280 dataset, the NRG1 was down-regulated in HCs compared to MDD patients before and after the treatment (FC=-0.44, p=0.04) and (FC=-1.44, p=0.001), respectively, while it was up-regulated when comparing MDD patients before and after Venlafaxine treatment (FC=1.5, p=0.001). Furthermore, the ML on 80% of the entire combined datasets including the discovery and external datasets along with performance evaluation in the 20% holdout showed that the best-built classifiers trained on the merged discovery dataset which consists of the LR best model along with LR as the feature selection method showed a 0.58 ± 0.05 F1 macro score on 5-fold cross-validation which is better than baseline with 0.38 ± 0.04 F1 macro score. Importantly, the NRG1 gene was considered among the most performing genes to distinguish between MDD patients and HC groups among the independent populations with diverse ethnicities. Taking these results together, it was suggestive of NRG1 as a potential robust biomarker to differentiate between MDD patients and HCs under different depression-related conditions as well as to follow up the MDD patients’ remission. NRG1 is a liquid biopsy and brain-related biomarker

[0111] To evaluate the potential of the NRG1 gene as an MDD-related diagnosis biomarker, first, its gene expression was checked in the Human brain using the AHBA. The NRG1 expression was assessed among transcriptomic data of six post-mortem brains from health samples without known neuropsychiatric or neuropathological disorders history. As shown in Figure 5, the NRG1 is expressed in the left cortex and subcortex with variable intensity amongthe six studied samples, suggesting that the NRG1 is a brain-related biomarker that could be informative upon a mental disorder such as depression. Due to the inaccessibility of the brain tissues, saliva biomarkers are considered the ultimate non-invasive alternative for mental- related diseases. To assess the possibility of using NRG1 as a potent MDD-related biomarker in saliva samples, the presence of NRGJ was investigated using quantitative RT-PCR on 12 patients with MDD and 8 HCs from the Kazakh population. The latter has a particular population ethnicity and is different from the other populations that were explored in the discovery and external datasets. The relative quantification of NRG1 gene expression after normalization to the reference gene 18S rRNA showed an up-regulation by the factor 1.48 in MDD patients compared to HCs (Figure 6).

[0112] While the above represents only a portion of the large volume of data that was generated while determining the suitability of different enzymes with varying degrees of impact on solubility of the studied proteins, in order to maintain brevity of the description, the above data is reflective of the output provided by the system, wherein the above listed enzymes were grouped into 3 different groups based on their respective rankings, indicating, at least based on the above, the presence of 3 separate proteins or protein consortia in whey protein powder.

[0113] The above is depictive of the versatility of the method and the system of the present disclosure to multiple types of proteins and protein consortia, independent of their source.

[0114] The foregoing description of the specific embodiments fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments in this disclosure have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

[0115] While considerable emphasis has been placed herein on the particular features of this disclosure, it will be appreciated that various modifications can be made, and that many

Claims

changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other modifications in the nature of the disclosure or the preferred embodiments will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.[00116] All references, articles, publications, general disclosures etc. cited herein are incorporated by reference in their entireties for all purposes. However, mention of any reference, article, publication etc. cited herein is not, and should not be taken as, an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world.WE CLAIM: A method for identifying biomarker(s) for detection of a mental health disorder, the method comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model is implemented to create an environment such that a learning agent, associated with the environment, is configured to:determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; and identify, based on the similarity score, a first set of biomarker(s) indicative of the mental health disorder(s); and the bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarker(s) indicative of the mental health disorder(s); and determining, based on an overlap between the first set of biomarker(s) and the second set of biomarker(s), the biomarker(s) for detection of the mental health disorder(s).

2. The method as claimed in claim 1, wherein implementing the biomarker identification model comprises applying a machine learning (ML) model, based on the preprocessed dataset, to create an environment, wherein the learning agent is associated with the environment, the learning agent being configured to: determine patterns and relationships among the one or more input features that are indicative of the mental health disorder(s); computing a similarity score of the determined patterns and relationships based on related contribution to the detection of the mental health disorder(s); and identifying, based on the associated similarity scores, one or more target input features that are indicative of the mental health disorder(s).

3. The method as claimed in claim 1, wherein the biomarker identification model comprises two or more machine learning (ML) models, to create the environment, wherein the learning agent is associated with the environment, the learning agent being configured to: select genes altered by the mental health disorder(s) based on individual outputs of the two or more ML models;perform hyperparameter optimization for the individual outputs of the two or more ML models; perform cross-validation on the individual outputs of the two or more ML models; and identifying, based on the cross-validation, the first set of biomarker(s) that are indicative of the mental health disorder.

4. The method as claimed in claim 1, wherein the method comprises: implementing a training agent in the environment of the biomarker identification model, wherein the training agent is configured to train the learning agent using information of the set of healthy reference parameters associated with the one or more healthy controls, and the set of diseased reference parameters associated with the one or more diseased subjects from the repository dataset.

5. The method as claimed in claim 1, wherein the similarity score of the biomarker(s) is determined by: identifying patterns and relationships among the one or more input features that are indicative of the mental health disorder.

6. The method as claimed in claim 1, wherein the dataset comprising biological and clinical data, wherein the biological data includes genetic, proteomic, metabolic, and neuroimaging information, and the clinical data includes behavioral and psychological parameters.

7. The method as claimed in claim 1, wherein preprocessing of the dataset comprises: performing data analysis on the dataset according to a data normalization rule to obtain normalized and regularized dataset, wherein the data normalization rule comprises a data classification rule and one or more data filtering conditions.

8. The method as claimed in claim 1, wherein the mental health disorder(s) is selected from a group comprising major depressive disorder (MDD), anxiety, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and bipolar disorder.

9. The method as claimed in claim 1, wherein the dataset further comprises contextual features, wherein the contextual features are associated with one or more biological parameters and clinical parameters related to the mental health disorder(s).

10. The method as claimed in claim 1, further comprising: generating a mapping profile of the determined biomarkers with corresponding brain regions and structural alterations thereof; and associating the mapping profile with cognitive functions and varied range of behaviors.

11. The method as claimed in claim 1, wherein the mental health disorder is MDD and wherein determined biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBSlor any combination thereof.

12. A method for determining level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1, in a biological sample obtained from a subject.

13. The method as claimed in claim 12, wherein the biological sample is a liquid biopsy sample.

14. The method as claimed in claim 13, wherein the liquid biopsy sample is selected from a group comprising blood sample, saliva sample.

15. The method as claimed in claim 12 wherein the level of biomarker(s) is determined by PCR.

16. The method as claimed in claim 12 for use in one or more of: a) diagnosing MDD; b) diagnosing and treating MDD; c) determining genetic predisposition to MDD; and d) monitoring response to MDD therapy in patients diagnosed with MDD.

17. A method for diagnosing mental health disorder(s), the said method comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method as claimed in claim 1.

18. The method as claimed in claim 17, wherein the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRGE 374 0EFM4, SERPING1, TCN1, and THBSE19. A method of diagnosing and treating mental health disorder(s) in a subject, comprising:- analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) identified by the method as claimed in claim 1, compared to a healthy control;- diagnosing the subject with the mental health disorder(s) once differential expression level of the one or more biomarker(s) is confirmed; and- administering therapy for the mental health disorder(s) to the diagnosed subject.

20. The method as claimed in claim 19, wherein the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRGE 374 OEFM4, SERPING1, TCN1, and THBSE21. The method as claimed in 20, wherein the therapy includes administration of Venlafaxine to the diagnosed subject.

22. A method for identifying genetic predisposition of a subject to mental health disorder(s) comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method as claimed in claim 1.

23. The method as claimed in claim 22, wherein the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRGE 374 OLFM4, SERPINGE TCNE and THBSE24. A kit for determining differential expression level of one or more biomarker(s) identified by the method as claimed in claim 1 in a biological sample, for detection of mental health disorder(s) in a biological sample, comprising at least one amplification primer and / or at least one probe that hybridizes to nucleotide(s) encoding the one or more biomarker(s).

25. A kit for determining differential expression level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRGE 374 OLFM4,SERPING1, TCN1, and THBS1 in a biological sample, comprising at least one amplification primer and / or at least one probe that hybridizes to nucleotide(s) encoding the one or more biomarker(s).

26. The kit as claimed in claim 25 wherein the amplification primers for NRG1 gene expression evaluation comprise Forward:5'-TCGTGGAATCAAACGCTACA-3' and Reverse:5'- ACTCCCCTCCATTCACACA-3'. s27. Use of biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBS1 or any combination thereof, for the diagnosis of MDD.

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