System and method for mental illness diagnosis

Abstract

The presently disclosed subject matter relates to a system and method for determining whether a subject is suffering from a mental illness. The system and method comprising a processing circuitry configured to: obtain: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determine, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

Description

[0001] SYSTEM AND METHOD FOR MENTAL ILLNESS DIAGNOSIS

[0002] TECHNICAL FIELD

[0003] The present invention relates to the field of mental illnesses, and more particularly, to the field of mental illness diagnosis.

[0004] BACKGROUND

[0005] A mental disorder, also referred to as a mental illness, a mental health condition, or a psychiatric disability, is a behavioral or mental pattern which causes significant distress or impairment of personal functioning. A mental disorder may be characterized by a clinically significant disturbance in an individual's cognition, emotional regulation, or behavior, often in a social context.

[0006] Such disturbances may occur as single episodes, may be persistent, or may be relapsing-remitting. There are many different types of mental disorders, with signs and symptoms that vary widely between specific disorders.

[0007] Disorders are usually diagnosed or assessed by a mental health professional, e.g., a clinical psychologist, psychiatrist, psychiatric nurse, or clinical social worker, using various methods (such as psychometric tests, and the like), but often relying on observation and questioning.

[0008] Misdiagnosis in the field of mental disorders is a common phenomenon with severe consequences. There are estimates that approximately 50% of medical treatments are error-prone, leading to ineffective treatments and poor outcomes.

[0009] One of the main reasons for misdiagnosis lies in the subjective assessment methods currently in use. As psychiatric diagnosis relies heavily on reports from the patient and his environment, as well as on the mental health professional's observations, these reports and observations tend to be open to subjective interpretation and bias.

[0010] Moreover, mental disorders often show similar and overlapping symptoms, making it difficult to determine the patient's mental state accurately. Currently, there are still no objective tools for mental diagnosis, similar to blood tests or medical imaging used in other medical fields.

[0011] Considering the above, there is a need in the art for a system and method for mental illness diagnosis. GENERAL DESCRIPTION

[0012] In accordance with a first aspect of the presently disclosed subject matter, there is provided a system for determining whether a subject is suffering from a mental illness, the system comprising a processing circuitry configured to: obtain: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determine, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

[0013] In some cases, the mental illness is one of: depression, bipolar disorder, anxiety disorder, paranoia, post-traumatic stress disorder, psychosis, or schizophrenia.

[0014] In some cases, the system also obtains (iii) psychometrical information associated with said subject, so that said machine learning model is trained and capable of determining whether said subject is suffering from said mental illness based on said psychometrical information.

[0015] In some cases, the psychometrical information includes at least one of: medical information, physiological information, psychological information, or sociological information.

[0016] In some cases, the genetic and epigenetic information of said subject are derived from a biological specimen acquired from said subject.

[0017] In some cases, the deriving of said genetic and epigenetic information is performed by executing genetic and epigenetic sequencing on said specimen.

[0018] In some cases, the genetic information of said subject contains information regarding one or more nucleotides of said subject's genome located within said corresponding genomic positions, and wherein said epigenetic information of said subject contains information regarding presence or absence of one or more chemical groups linked to said one or more nucleotides within said corresponding genomic positions.

[0019] In some cases, the one or more chemical groups includes at least one of: Methyl group, hydroxymethyl group, or acetyl group. In some cases, the epigenetic information of the subject contains information regarding presence or absence of one or more epigenetic modifications of: Histone acetylation, Histone methylation, Histone phosphorylation, Histone ubiquitination, Histone sumoylation, Histone crotonylation, Histone citrullination, Histone ADP- ribosylation, Histone glycosylation, Histone serotonylation, RNA methylation (e.g., m6A methylation), Chromatin remodeling, or Non-coding RNA-associated gene silencing, associated with the one or more nucleotides within the corresponding genomic positions.

[0020] In accordance with a second aspect of the presently disclosed subject matter, there is provided a method for determining whether a subject is suffering from a mental illness comprising: obtaining: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determining, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

[0021] In some cases, the mental illness is one of: depression, bipolar disorder, anxiety disorder, paranoia, post-traumatic stress disorder, psychosis, or schizophrenia.

[0022] In some cases, the system also obtains (iii) psychometrical information associated with said subject, so that said machine learning model is trained and capable of determining whether said subject is suffering from said mental illness based on said psychometrical information.

[0023] In some cases, the psychometrical information includes at least one of: medical information, physiological information, psychological information, or sociological information.

[0024] In some cases, the genetic and epigenetic information of said subject are derived from a biological specimen acquired from said subject.

[0025] In some cases, the deriving of said genetic and epigenetic information is performed by executing genetic and epigenetic sequencing on said specimen.

[0026] In some cases, the genetic information of said subject contains information regarding one or more nucleotides of said subject's genome located within said corresponding genomic positions, and wherein said epigenetic information of said subject contains information regarding presence or absence of one or more chemical groups linked to said one or more nucleotides within said corresponding genomic positions.

[0027] In some cases, the one or more chemical groups includes at least one of: Methyl group, hydroxymethyl group, or acetyl group.

[0028] In some cases, the epigenetic information of the subject contains information regarding presence or absence of one or more epigenetic modifications of: Histone acetylation, Histone methylation, Histone phosphorylation, Histone ubiquitination, Histone sumoylation, Histone crotonylation, Histone citrullination, Histone ADP- ribosylation, Histone glycosylation, Histone serotonylation, RNA methylation (e.g., m6A methylation), Chromatin remodeling, or Non-coding RNA-associated gene silencing, associated with the one or more nucleotides within the corresponding genomic positions.

[0029] In accordance with a third aspect of the presently disclosed subject matter, there is provided a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code, executable by at least one processor of a computer to perform a method for determining whether a subject is suffering from a mental illness, the method comprising: obtaining: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determining, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to understand the presently disclosed subject matter and to see how it may be carried out in practice, the subj ect matter will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which: Fig- 1 is a schematic illustration of an environment in which an exemplary system for mental illness diagnosis, in accordance with the presently disclosed subject matter, operates;

[0032] Fig- 2 is a block diagram schematically illustrating one example of components of an exemplary system for mental illness diagnosis, in accordance with the presently disclosed subject matter; and,

[0033] Fig- 3 is a flowchart illustrating an example of a sequence of operations carried out by an exemplary system for mental illness diagnosis, in accordance with the presently disclosed subject matter.

[0034] DETAILED DESCRIPTION

[0035] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the presently disclosed subject matter. However, it will be understood by those skilled in the art that the presently disclosed subject matter may be practiced without these specific details. In other instances, well- known methods, procedures, and components have not been described in detail so as not to obscure the presently disclosed subject matter.

[0036] In the drawings and descriptions set forth, identical reference numerals indicate those components that are common to different embodiments or configurations.

[0037] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “obtaining”, “determining”, “deriving”, “executing”, or the like, include action and / or processes of a computer that manipulate and / or transform data into other data, said data represented as physical quantities, e.g., such as electronic quantities, and / or said data representing the physical objects. The terms “computer”, “processor”, “processing resource”, “processing circuitry”, and “controller” should be expansively construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, a personal desktop / laptop computer, a server, a computing system, a communication device, a smartphone, a tablet computer, a smart television, a processor (e.g. digital signal processor (DSP), a microcontroller, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.), a group of multiple physical machines sharing performance of various tasks, virtual servers co- residing on a single physical machine, any other electronic computing device, and / or any combination thereof.

[0038] The operations in accordance with the teachings herein may be performed by a computer specially constructed for the desired purposes or by a general-purpose computer specially configured for the desired purpose by a computer program stored in a non- transitory computer readable storage medium. The term "non-transitory" is used herein to exclude transitory, propagating signals, but to otherwise include any volatile or nonvolatile computer memory technology suitable to the application.

[0039] As used herein, the phrase "for example," "such as", "for instance" and variants thereof describe non-limiting embodiments of the presently disclosed subject matter. Reference in the specification to "one case", "some cases", "other cases" or variants thereof means that a particular feature, structure or characteristic described in connection with the embodiment(s) is included in a least one embodiment of the presently disclosed subject matter. Thus, the appearance of the phrase "one case", "some cases", "other cases" or variants thereof does not necessarily refer to the same embodiment s).

[0040] It is appreciated that, unless specifically stated otherwise, certain features of the presently disclosed subject matter, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the presently disclosed subject matter, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination.

[0041] In embodiments of the presently disclosed subject matter, fewer, more and / or different stages than those shown in Fig. 3 may be executed. In embodiments of the presently disclosed subject matter one or more stages illustrated in Fig. 3 may be executed in a different order and / or one or more groups of stages may be executed simultaneously. Fig- 1 illustrate a general schematic of the system architecture in accordance with an embodiment of the presently disclosed subject matter. Each module in Fig. 2 may be made up of any combination of software, hardware and / or firmware that performs the functions as defined and explained herein. The modules in Fig. 2 may be centralized in one location or dispersed over more than one location. In other embodiments of the presently disclosed subject matter, the system may comprise fewer, more, and / or different modules than those shown in Fig. 2. Any reference in the specification to a method should be applied mutatis mutandis to a system capable of executing the method and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that once executed by a computer result in the execution of the method.

[0042] Any reference in the specification to a system should be applied mutatis mutandis to a method that may be executed by the system and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that may be executed by the system.

[0043] Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a system capable of executing the instructions stored in the non-transitory computer readable medium and should be applied mutatis mutandis to method that may be executed by a computer that reads the instructions stored in the non-transitory computer readable medium.

[0044] By way of introduction, as mentioned hereinbefore, misdiagnosis in the field of mental disorders is a common phenomenon with potentially devastating results. Having the wrong mental diagnosis often results in patients receiving treatment that is not appropriate for their condition, causing a waste of time and resources and harming their quality of life.

[0045] A wrong drug treatment can lead to severe side effects and even worsen mental health. Additionally, a wrong diagnosis can lead to significant financial consequences, both for patients and for the health care system as a whole. In the US alone, misdiagnosis of mental disorders costs health care providers approximately $90 billion per year.

[0046] As the prevalence of reported mental disorders is on the rise, it is imperative that healthcare providers and professionals find innovative diagnostic solutions that can provide an accurate and appropriate response, thus reducing unnecessary ancillary costs that burden healthcare systems financially and allocate resources inappropriately.

[0047] Bearing this in mind, attention is drawn to Fig. 1, showing a schematic illustration of an environment in which a system for mental illness diagnosis, in accordance with the presently disclosed subject matter, operates.

[0048] As shown in the schematic illustration, environment 100 includes a subject, denoted 102, suspected of suffering from mental illness. It is to be of note that although the following description refers to a single subject the same may also apply for a group of subjects. Subject 102 may undergo one or more medical procedures directed for acquiring at least one biological specimen, denoted 104. In one non-limiting example, the one or more medical procedures may include a saliva sampling procedure in which subject 102 may be instructed to push saliva into a collection tube. In another non-limiting example, the one or more medical procedures may include a blood collection procedure, in which blood may be extracted from said subject vein.

[0049] It is to be of note that the examples above are presented merely for the purpose of better understanding the presently disclosed subject matter and are not intended in any way to limit its scope. It is to be further of note that other types of biological testing, such as urine testing, stool testing, and the like, may also be applicable.

[0050] Once acquired, the at least one biological specimen 104 may be utilized for deriving biological information, denoted 106, associated with said subject. In one nonlimiting example, the derived biological information may be genetic information (e.g., DNA sequence, RNA sequence, etc.) acquired by executing genetic sequencing on said specimen, using one or more appropriate genetic sequencing techniques (e.g., DNA sequencing techniques, such as Nanopore DNA sequencing, etc.). In another non-limiting example, alternatively or additionally to the above, the derived biological information may be epigenetic information (e.g., DNA methylation throughout the DNA, etc.) acquired by executing epigenetic sequencing on said specimen, using one or more appropriate epigenetic sequencing techniques (e.g., Next Generation Sequencing (NGS) techniques, such as methyl-seq, ChlP-seq, ATAC-seq, etc., Nanopore sequencing, or any long or short read sequencing technique).

[0051] It is possible that in addition to said information derived from said biological specimen, subject 102 may be required to provide additional information, such as psychometrical information (e.g., psychological information (for example, depression episodes within a certain time period, occurrence of depressional thoughts within a certain time period, occurrence of suicidal thoughts within a certain time period, etc.)), sociological information (e.g., monthly income, family status, etc.), and the like), via, for example, completing appropriate questionnaires.

[0052] It is further possible that in addition to said information derived from said biological specimen, additional information associated with said subject, such as medical information, may be obtained. In one non-limiting example, said medical information may be related to past and / or current medical conditions, obtained, for example, from said subject's medical records. In another non-limiting example, alternatively or additionally to the above, said medical information may include physiological indices, such as heart rate, blood pressure, body temperature, oxygen saturation, respiratory rate, muscle strength, metabolic rate, and hormonal levels, etc., acquired via dedicated means (e.g., an oxygen saturation monitor, a sphygmomanometer, a body temperature monitor, and the like).

[0053] It is to be of note that the examples above are presented merely for the purpose of better understanding the presently disclosed subject matter and are not intended in any way to limit its scope. It is to be further of note that other types of information associated with said subject may also be applicable.

[0054] Attention is now drawn to a description of components of a system for mental illness diagnosis 200.

[0055] Fig- 2 is a block diagram schematically illustrating one example of the system for mental illness diagnosis 200, in accordance with the presently disclosed subject matter.

[0056] In accordance with the presently disclosed subject matter, the system for mental illness diagnosis 200 (also interchangeably referred to herein as “system 200”) may comprise a network interface 206. The network interface 206 (e.g., a network card, a WiFi client, 3G / 4G client, or any other component), enables system 200 to communicate over a network with external systems and handles inbound and outbound communications from such systems. For example, system 200 may receive, through network interface 206, genetic and / or epigenetic information associated with one or more subjects potentially suffering from a mental illness.

[0057] System 200 may further comprise or be otherwise associated with a data repository 204 (e.g., a database, a storage system, a memory including Read Only Memory - ROM, Random Access Memory - RAM, or any other type of memory, etc.) configured to store data. Some examples of data that may be stored in the data repository 204 include:

[0058] • Genetic information of one or more subjects potentially suffering from a mental illness;

[0059] • Genetic information of one or more subj ects known to suffer from a mental illness;

[0060] • Epigenetic information of one or more subjects potentially suffering from a mental illness; • Epigenetic information of one or more subjects known to suffer from a mental illness;

[0061] • Psychometrical information of one or more subjects potentially suffering from a mental illness;

[0062] • Psychometrical information of one or more subjects known to suffer from a mental illness;

[0063] • One or more machine learning models capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from a given mental illness;

[0064] • One or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from a given mental illness;

[0065] • One or more mental illnesses associated with one or more subjects; etc.

[0066] Data repository 204 may be further configured to enable retrieval and / or update and / or deletion of the stored data. It is to be noted that in some cases, data repository 204 may be distributed, while the system 200 has access to the information stored thereon, e.g., via a wired or wireless network to which system 200 is able to connect (utilizing its network interface 206).

[0067] System 200 further comprises processing circuitry 202. Processing circuitry 202 may be one or more processing units (e.g., central processing units), microprocessors, microcontrollers (e.g., microcontroller units (MCUs)) or any other computing devices or modules, including multiple and / or parallel and / or distributed processing units, which are adapted to independently or cooperatively process data for controlling relevant system 200 resources and for enabling operations related to system’s 200 resources.

[0068] The processing circuitry 202 comprises a mental illness determination module 208, configured to perform a mental illness determination process, as further detailed herein, inter alia with reference to Fig. 3.

[0069] Turning to Fig. 3 there is shown a flowchart illustrating one example of operations carried out by system for mental illness diagnosis 200, in accordance with the presently disclosed subject matter.

[0070] Accordingly, the system for mental illness diagnosis 200 may be configured to perform a mental illness determination process 300, e.g., using mental illness determination module 208. For this purpose, system 200 obtains: (i) genetic and epigenetic information of a subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from a mental illness (block 302).

[0071] Once obtained, system 200 determines, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said given mental illness (block 304).

[0072] In some cases, the genetic information of said subject may contain, for example, information regarding one or more nucleotides of said subject's genome located within corresponding genomic positions. In such cases, said epigenetic information of said subject may contain information regarding presence or absence of one or more chemical groups (e.g., Methyl group, hydroxymethyl group, acetyl group, or any molecule interacting with DNA, RNA, or protein to directly or indirectly manipulate gene expression etc.) linked to said one or more nucleotides within said corresponding genomic positions. In other cases, alternatively or additionally to the above, said epigenetic information of said subject may contain information regarding presence or absence of one or more epigenetic modifications, such as Histone acetylation, Histone methylation, Histone phosphorylation, Histone ubiquitination, Histone sumoylation, Histone crotonylation, Histone citrullination, Histone ADP-ribosylation, Histone glycosylation, Histone serotonylation , RNA methylation (e.g., m6A methylation), Chromatin remodeling, Non-coding RNA-associated gene silencing, etc., associated with said one or more nucleotides within said corresponding genomic positions.

[0073] In some cases, the machine learning model may be trained based on one or more features, at least some of which may be generated, for example, by analyzing genetic and epigenetic information of a plurality of subjects known to suffer from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes.

[0074] By way of a non-limiting example, presented merely for better understanding of the disclosed subject matter and not intended in any way to limit its scope, assuming position 1,000,000 of the genome of said plurality of subjects known to suffer from said mental illness contains the nucleotide base "A" with 100 percent of the "A"s having a Methyl group mounted thereon, the machine learning model may be trained to classify a given subject with a Methyl group attached to nucleotide base "A" at position 1,000,000 as suffering from the same mental illness.

[0075] In some cases, the mental illness may be any one of: depression, bipolar disorder, anxiety disorder, paranoia, post-traumatic stress disorder, psychosis, or schizophrenia. In other cases, said mental illness may be any other mental illness / disorders as described by the Diagnostic and Statistical Manual of Mental Disorders (DSM), the International Classification of Diseases (ICD), or any other manual for metal disorders known in the art.

[0076] In some cases, system 200 may further obtain psychometrical information associated with said subject, so that the machine learning model may be trained and capable of determining whether said subject is suffering from said mental illness based on said psychometrical information.

[0077] It is to be of note that the above serves as a mere example and that other types of information associated with said subject may also be applicable.

[0078] It is to be of note that, in some cases, alternatively to the above, the machine learning model may be trained to predict the chances of a given subject developing a given mental illness. In such cases, the machine learning model may yet again be trained based on one or more features, at least some of which may be generated, for example, by analyzing genetic and epigenetic information of a plurality of subjects known to suffer from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes.

[0079] It is to be noted, with reference to Fig- 3, that some of the blocks may be integrated into a consolidated block or may be broken down to a few blocks and / or other blocks may be added. It is to be further noted that some of the blocks are optional. It should be also noted that whilst the flow diagram is described also with reference to the system elements that realizes them, this is by no means binding, and the blocks may be performed by elements other than those described herein.

[0080] It is to be understood that the presently disclosed subject matter is not limited in its application to the details set forth in the description contained herein or illustrated in the drawings. The presently disclosed subject matter is capable of other embodiments and of being practiced and carried out in various ways. Hence, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for designing other structures, methods, and systems for carrying out the several purposes of the present presently disclosed subject matter.

[0081] It will also be understood that the system according to the presently disclosed subject matter may be implemented, at least partly, as a suitably programmed computer. Likewise, the presently disclosed subject matter contemplates a computer program being readable by a computer for executing the disclosed method. The presently disclosed subject matter further contemplates a machine-readable memory tangibly embodying a program of instructions executable by the machine for executing the disclosed method.

Claims

CLAIMS:

1. A system for determining whether a subject is suffering from a mental illness, the system comprising a processing circuitry configured to: obtain: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determine, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

2. The system of claim 1, wherein said mental illness is one of: depression, bipolar disorder, anxiety disorder, paranoia, post-traumatic stress disorder, psychosis, or schizophrenia.

3. The system of claim 1, wherein said system also obtains (iii) psychometrical information associated with said subject, so that said machine learning model is trained and capable of determining whether said subject is suffering from said mental illness based on said psychometrical information.

4. The system of claim 3, wherein said psychometrical information includes at least one of: medical information, physiological information, psychological information, or sociological information.

5. The system of claim 1, wherein said genetic and epigenetic information of said subject are derived from a biological specimen acquired from said subject.

6. The system of claim 5, wherein said deriving of said genetic and epigenetic information is performed by executing genetic and epigenetic sequencing on said specimen.

7. The system of claim 1, wherein said genetic information of said subject contains information regarding one or more nucleotides of said subject's genome located within said corresponding genomic positions, and wherein said epigenetic information of said subject contains information regarding presence or absence of one or more chemical groups linked to said one or more nucleotides within said corresponding genomic positions.

8. The system of claim 7, wherein said one or more chemical groups includes at least one of: methyl group, hydroxymethyl group, or acetyl group.

9. The system of claim 7, wherein said epigenetic information of said subject contains information regarding presence or absence of one or more epigenetic modifications of: Histone acetylation, Histone methylation, Histone phosphorylation, Histone ubiquitination, Histone sumoylation, Histone crotonylation, Histone citrullination, Histone ADP-ribosylation, Histone glycosylation, Histone serotonylation, RNA methylation (e.g., m6A methylation), Chromatin remodeling, or Non-coding RNA- associated gene silencing, associated with said one or more nucleotides within said corresponding genomic positions.

10. A method for determining whether a subject is suffering from a mental illness comprising: obtaining: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and,determining, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

11. The method of claim 10, wherein said mental illness is one of: depression, bipolar disorder, anxiety disorder, paranoia, post-traumatic stress disorder, psychosis, or schizophrenia.

12. The method of claim 10, wherein said system also obtains (iii) psychometrical information associated with said subject, so that said machine learning model is trained and capable of determining whether said subject is suffering from said mental illness based on said psychometrical information.

13. The method of claim 12, wherein said psychometrical information includes at least one of: medical information, physiological information, psychological information, or sociological information.

14. The method of claim 10, wherein said genetic and epigenetic information of said subject are derived from a biological specimen acquired from said subject.

15. The method of claim 14, wherein said deriving of said genetic and epigenetic information is performed by executing genetic and epigenetic sequencing on said specimen.

16. The method of claim 10, wherein said genetic information of said subject contains information regarding one or more nucleotides of said subject's genome located within said corresponding genomic positions, and wherein said epigenetic information of said subject contains information regarding presence or absence of one or more chemical groups linked to said one or more nucleotides within said corresponding genomic positions.

17. The method of claim 16, wherein said one or more chemical groups includes at least one of: Methyl group, hydroxymethyl group, or acetyl group.

18. The method of claim 16, wherein said epigenetic information of said subject contains information regarding presence or absence of one or more epigenetic modifications of: Histone acetylation, Histone methylation, Histone phosphorylation, Histone ubiquitination, Histone sumoylation, Histone crotonylation, Histone citrullination, Histone ADP-ribosylation, Histone glycosylation, Histone serotonylation, RNA methylation (e.g., m6A methylation), Chromatin remodeling, or Non-coding RNA- associated gene silencing, associated with said one or more nucleotides within said corresponding genomic positions.

19. A non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code, executable by at least one processor of a computer to perform a method for determining whether a subject is suffering from a mental illness, the method comprising: obtaining: (i) genetic and epigenetic information of said subject, and (ii) a machine learning model capable of receiving genetic and epigenetic information of a given subject and determining whether said given subject is suffering from said mental illness, wherein said machine learning model is trained based on one or more features generated by analyzing genetic and epigenetic information of a plurality of subjects suffering from said mental illness, associated with one or more corresponding genomic positions of said plurality of subjects' genomes; and, determining, using said machine learning model and said genetic and epigenetic information of said subject, whether said subject is suffering from said mental illness.

Images