30 results about "Biomarker identification" patented technology

The invention relates to the technical field of intelligent diagnosis systems for neurodegenerative diseases, and discloses a system and equipment for identifying an early biomarker of Parkinson's disease based on non-invasive electroencephalogram. The Parkinson's disease early-stage biomarker identification system comprises a multi-mode signal acquisition module which is provided with a 64-lead dry electrode electroencephalogram cap, an integrated preamplifier and an ADC converter and is used for synchronously acquiring resting-state electroencephalogram and event-related potential, dynamically completing switching of the resting-state electroencephalogram sampling rate and transmitting acquired original data to a preprocessing module. According to the method, revolutionary improvement is realized in three dimensions of acquisition, analysis and calculation through system-level collaborative innovation, a nanocrystalline shielding dry electrode is combined with a dynamic impedance adjustment technology, so that the signal-to-noise ratio of a home environment is increased to 28dB, and the pathological correlation of key markers such as frontal lobe-basal node loop gamma entropy is verified by Granger causality and has a good application prospect. The cloud distributed model server supports 2000 paths of concurrent analysis, and the real-time bottleneck of home screening is thoroughly solved.

A method and system for the use of multi-spectral retinal images to achieve an effective, efficient, and AI enabled automated retinal disease biomarker detection using biomarker identification, segmentation, and quantification. A plurality of digital multi-spectral images of a retina of a patient at a plurality of illumination wavelengths can be done using a multi-spectral ophthalmoscope. The images can then be registered, processed, and assessed to automatically quantifying one or more biomarker based on the location and size of the biomarker in the plurality of processed multi-spectral images.

The invention provides a Brown relaxation dominated magnetic nanoparticle nonlinear biological information parameter estimation method. The method comprises the following steps: putting a container of a magnetic nanoparticle sample into a to-be-detected object area; applying low and medium frequency alternating current magnetic field excitation to the magnetic nanoparticle sample; collecting magnetization response information of the magnetic nanoparticle sample in real time and extracting harmonic amplitude information from the magnetization response information; carrying out series expansion on the Fokker-Planck equation by using a disturbance expansion method, and deducing a harmonic analytical expression under the Brown relaxation condition; respectively establishing a corresponding temperature estimation model and a hydrodynamic diameter estimation model on the basis of the harmonic analytical expression; respectively substituting the measured harmonic amplitude information into a temperature estimation model or a hydrodynamic diameter estimation model based on a harmonic analytical expression; and respectively solving an equation set formed by the temperature estimation model or the hydrodynamic diameter estimation model to obtain temperature information or hydrodynamic diameter information. According to the invention, the measurement precision and the time resolution of the magnetic nanometer temperature measurement and biomarker identification tool can be improved.

The invention discloses an infantile autism spectrum disorder identification system based on a graph neural network, and the system specifically comprises a data processing module which is used for extracting time sequence information from fMRI data and constructing a brain function connection graph; the model training module is configured with an unsupervised graph auto-encoder GAE and comprises a three-layer graph convolutional network GCN as an encoder and an inner product decoder; the biomarker recognition module is used for executing a replacement test and screening brain regions with significant differences; and the performance evaluation module is used for evaluating the diagnostic performance of the system and the effectiveness of biomarker discovery on the ABIDE data set. The invention provides an ASD identification solution which does not need manual annotation, is high in interpretability and excellent in performance, and has important significance for promoting the development of ASD early diagnosis and personalized treatment.

A method and system for the use of multi-spectral retinal images to achieve an effective, efficient, and AI enabled automated retinal disease biomarker detection using biomarker identification, segmentation, and quantification. A plurality of digital multi-spectral images of a retina of a patient at a plurality of illumination wavelengths can be done using a multi-spectral ophthalmoscope. The images can then be registered, processed, and assessed to automatically quantifying one or more biomarker based on the location and size of the biomarker in the plurality of processed multi-spectral images.

The invention provides biomarkers to predict effective treatment of cancer using an IAP antagonist. Identifying one or more of the identified biomarkers in a cancer patient allows a determination to be made whether the patient's cancer is likely to be successfully treated using an IAP antagonist. Accordingly, the invention relates generally to a companion diagnostic for IAP antagonist therapy. In particular, the biomarkers include IL-2Rα, IL-6r, CRP, EPO, CD27, CD40, TN-C, IL-18, PLGF, TNFrl and MCP-4.

Systems and methods for determining the health status of a retina of a subject. An optical coherence tomography (OCT) volume image of a retina of a subject may be received. A health indication output is generated, via a deep learning model, using the OCT volume image. The health indication output indicates a level of association between the OCT volume image and a selected health status category for the retina. A map output for the deep learning model is generated using a saliency mapping algorithm, generating a map output for the deep learning model using a saliency mapping algorithm. The map output indicates a level of contribution of a set of regions in the OCT volume image to the health indication output generated by the deep learning model.

The application discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, belongs to the field of medical imaging and tumor radiotherapy, and comprises an ultrahigh-field multi-modal image acquisition module, a multi-phase accurate registration and radiomics feature extraction module, an early response biomarker identification module and an adaptive treatment decision support module. The high resolution, high signal-to-noise ratio and multi-nuclide imaging capability of the 7T ultrahigh-field magnetic resonance are utilized to acquire multi-modal images before and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, the treatment response is predicted based on the Delta feature and the deep learning model 2 to 3 weeks after the start of radiotherapy, the prediction accuracy reaches 87.5%, the prediction is 3 to 4 weeks earlier than the conventional evaluation, decision support is provided for timely adjustment of the treatment strategy, a closed-loop feedback mechanism is established, the clinical verification results are used to optimize the parameters of each module reversely, and continuous improvement is realized.

The invention provides a biomarker identification method fusing multi-omics data. The biomarker identification method comprises the steps of data cleaning, normalization, integration, feature extraction and selection and the like. Data cleaning is carried out to remove noise and correct deviation, normalized scaling and standardized data distribution are carried out, different omics data are integrated and associated, and alignment processing is carried out; feature extraction is based on statistics, frequency domain and image analysis, and feature selection adopts filtering, wrapping and embedding methods; prediction model construction comprises integrated learning, deep learning and a multi-modal fusion model, and model training relates to data set division, parameter setting and training processes. According to the method, the signal-to-noise ratio and the integrity of the data are improved, potential correlation between omics data can be found, and the most representative biomarker features can be screened out.

A method, computer system, and a computer program product for biomarker identification is provided. The present invention may include generating a plurality of higher-order joint cumulants based on an input data matrix. The present invention may include identifying one or more significant higher-order joint cumulant groups from the plurality of higher-order joint cumulants. The present invention may include embedding the one or more significant higher-order joint cumulant groups into a lower dimensional network. The present invention may include identifying one or more biomarkers.

The invention discloses a method for identifying a repeatedly frozen and thawed meat product by screening differential markers based on lipidomics. The method comprises the following steps: pre-treating a sample; carrying out non-targeted component determination by an LC-MS / MS method; performing data preprocessing and multivariate statistical analysis; screening differential markers by taking variable importance projection indexes and the like as screening indexes; and carrying out targeted quantitative analysis on the key differential marker to confirm the reliability of identifying the repeated freezing and thawing of the meat product by using the key differential marker as a biomarker. By adopting the method, 55 differential markers are successfully screened, key biomarkers LPC (18: 1 / 0: 0) and myristic acid are determined, and the key biomarkers can be used for identifying repeated freezing and thawing of meat products through targeted quantitative analysis.

Identifying the causes of haematuria requires detailed clinical investigations. Unbalanced patient populations (mostly male) and diverse causes complicate patient classification and biomarker identification. Multi-marker analysis with advanced methods can improve diagnosis even in unbalanced datasets. We used k-means clustering, decision trees, random forest with LIME explainer, and CACTUS algorithm to classify patients as healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The CACTUS algorithm was more robust to dataset imbalance, achieving balanced accuracy of 0.747 for both genders, 0.718 for females, and 0.803 for males. Key biomarkers identified included microalbumin, male gender, and tPSA for the whole dataset; age, microalbumin, tPSA, cystatin C, BTA, HAD, and S100A4 for males; and microalbumin, IL-8, pERK, and CXCL16 for females. CACTUS outperformed decision trees and random forest, highlighting potential novel biomarkers for future diagnosis.

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

The invention discloses a method for detecting early hepatocellular carcinoma and a reagent set used by the method. The detection method comprises the following steps: performing biomarker identification of a group of methylated genes with difference in a computing system; performing, in a computing system, a quantitative measurement of the degree of methylation from the selected plurality of biomarkers in the group using a quantitative methylation-specific polymerase chain reaction; executing a formula calculation in the calculation system according to the methylation degree of the selected biomarker to obtain an M score of the selected biomarker; and performing a risk level assessment of hepatocellular carcinoma using the resulting M fraction or a combination of M fraction and serum alpha fetoprotein in a computing system.

The invention discloses a biomarker recognition system based on a graph convolutional neural network, and the system comprises the steps: carrying out the graph embedding of a gene regulatory network at different pathological stages, and obtaining the low-dimensional vector representation of a gene node in the gene regulatory network at different pathological stages; based on the low-dimensional vector representation of the gene nodes, clustering the gene nodes in different pathological stages by adopting a clustering algorithm; calculating a deviation score to measure the abnormal degree of the gene node in each pathological stage, and storing the gene of which the deviation score exceeds a set threshold value into an abnormal gene set; extracting a minimum dominating set in the abnormal gene set; adopting a shortest path algorithm to optimize connectivity between nodes of the minimum dominating set, and generating biomarker networks in different pathological stages; calculating dynamic network indexes based on the biomarker networks in different pathological stages; and determining a key time node of a disease development stage based on the change turning point of the dynamic network index.

Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Disclosed are compositions containing human pancreatic islet organoids. The disclosed human pancreatic islet organoid culture systems mimic the morphogenic event in the natural islet development process, thereby enabling spontaneous generation of islet structure with high efficiency and preserving crucial islet structure. Also disclosed are methods of generating human pancreatic islet organoids, wherein the methods require a six-stage culture system. Further disclosed are methods using the in vitro generated organoids and compositions thereof. The in vitro generated organoids are particularly suited for research purposes, providing optimized cell models for the study of pancreatic islet biology, endocrine biology and metabolic disease pathology. The in vitro generated organoids are also particularly suitable for biomarker identification, and evaluation of new molecules and existing therapies for treating metabolic and / or endocrine diseases.

According to the identification and application of the cell death and immunosuppression biomarkers of the sepsis patients in different life cycles, transcriptome data of the sepsis patients in different age groups and a normal control group are collected, differential expression gene analysis is performed by utilizing bioinformatics, immune cell infiltration is estimated in combination with a CIBERSORT algorithm, and the cell death and immunosuppression biomarkers of the sepsis patients in different life cycles are identified. And the relation between the DEGs and the specific immune cell subpopulation is discussed through correlation analysis. And taking an intersection with genes related to a programmed cell death mode, constructing a sepsis risk assessment model by using a machine learning algorithm, and quantifying the contribution of the genes to a sepsis prediction result through an SHAP algorithm. The constructed model is especially suitable for neonates, children and adult sepsis patients, can distinguish risk assessment of different age groups and identify key programmed cell death modes, provides new biomarkers and tools for early diagnosis, treatment and risk assessment of sepsis, and is beneficial to improvement of clinical treatment effect and patient prognosis.

The invention discloses an anti-aging polypeptide detection method and system based on Label-free. The method comprises the following steps: extracting protein in a sample for pretreatment; the method comprises the following steps: carrying out enzymolysis on a polypeptide sample stored in an acetic acid aqueous solution, and cutting protein into peptide fragments by using trypsin; carrying out mass spectrometry detection and analysis by using an improved Label-free technology; and performing layered search on the mass spectrum parameters by using an Ivy optimization algorithm to obtain a detection result of the anti-aging polypeptide. According to the invention, the accuracy and sensitivity of detection are improved, the cost is reduced, the interference of a marking agent is avoided, and a scientific basis is provided for research and development of anti-aging drugs and identification of biomarkers.

The invention relates to the technical field of computer technology and biomedicine cross application, in particular to a multi-task cooperation method combining disease progression prediction and multi-mode biomarker recognition, which comprises the following steps of: according to a multi-mode biomarker, longitudinal brain image data and cognitive behavior data, determining whether the multi-mode biomarker is identified or not; constructing a longitudinal disease progress prediction model based on the time series data; optimizing parameters in the prediction model based on the defined objective function according to the multi-modal biomarker and the longitudinal brain image data to obtain an optimized longitudinal disease progress prediction model; and predicting a new cognitive behavior score from the to-be-identified multi-modal biomarker and the longitudinal brain image data through the optimized longitudinal disease progress prediction model. By integrating image data, gene data and environment information, a multi-modal data fusion model is constructed, and intelligent prediction of disease progress trend and automatic identification of key risk factors are realized.

Schizophrenia (SCZ) is the most debilitating of the Serious Mental Illnesses, a group of disorders affecting 4.8% of Arizona adults. The lack of a biologically based test makes diagnosing SCZ difficult, particularly for the 40% of Arizonans in areas with inadequate mental health care. Even under ideal circumstances, it can take months to years to accurately diagnose SCZ and identify an effective medication regimen. There is a critical need to identify biological markers to rapidly diagnose SCZ, which will lead to faster symptom resolution and improved patient outcomes. Methods described herein capitalize on the long-recognized association between SCZ risk and immune system dysfunction by stimulating peripheral blood immune cells with immunogenic agents to gain insight into disrupted pathways of gene expression in the brain. The immune stimulation of peripheral blood cells produces a unique pattern of gene expression, which will differ between SCZ and healthy control subjects.

Provided herein are minimally invasive compositions, methods, systems, kits and uses for biomarkers derived from the plasma proteome that identify, predict, and monitor organ health, aging, dysfunction and disease in humans. Said methods comprise a) obtaining a sample from said subject; b) measuring the concentrations of two or more proteins from said organ in said sample from said subject wherein said concentrations of said two or more proteins provides a biological age of said organ in health and / or disease; and c) comparing said biological age of said organ to a chronological age of said subject, wherein a gap between said biological age of said organ and said chronological age of said subject identifies accelerated and slowed aging of said organ.

The present invention relates to a method of isolating exosomes. Specifically, the invention relates to a method comprising the steps of providing a sample including exosomes; identifying a cell-surface polypeptide on the exosomes; and isolating the exosomes using the cell-surface polypeptide on the exosomes. The exosomes isolated from by the methods of the invention can be studied for the purposes of biomarker identification, for the understanding of biological function and disease, and to find ways to target them with therapeutics.

A method of detecting early-stage hepatocellular carcinoma has steps of performing biomarker identification of a plurality of differentially methylated genes in a computing system; performing quantitative measurement of the methylation levels of the biomarkers selected from the group of differentially methylated genes with quantitative methylation-specific PCR in the computing system; performing calculation of a formula in the computing system to obtain M-score of the selected biomarkers according to the measured methylation levels of the selected biomarkers with a logistic regression analysis; and performing a risk level evaluation of liver cancer with the M-score of the selected biomarkers in the computing system.

The invention provides biomarkers to predict effective treatment of cancer using an IAP antagonist. Identifying one or more of the identified biomarkers in a cancer patient allows a determination to be made whether the patient's cancer is likely to be successfully treated using an IAP antagonist. Accordingly, the invention relates generally to a companion diagnostic for IAP antagonist therapy. In particular, the biomarkers include IL-2Rα, IL-6r, CRP, EPO, CD27, CD40, TN-C, IL-18, PLGF, TNFrl and MCP-4.

The invention discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, which belongs to the field of medical imaging and tumor radiotherapy and comprises an ultrahigh-field multi-modal image acquisition module, a multi-temporal precise registration and radiomics feature extraction module, an early response biomarker recognition module and a self-adaptive treatment decision support module. The high resolution, the high signal-to-noise ratio and the multi-nuclide imaging capability of 7T ultrahigh-field magnetic resonance are utilized, multi-modal images are collected before treatment and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, treatment response is predicted 2-3 weeks at the beginning of radiotherapy based on Delta features and a deep learning model, the prediction accuracy rate reaches 87.5%, and the prediction result is accurate. The method is 3-4 weeks earlier than conventional evaluation, decision support is provided for timely adjustment of treatment strategies, a closed-loop feedback mechanism is established for the system, parameters of all modules are reversely optimized according to clinical verification results, and continuous improvement is achieved.

The application discloses a multi-modal critical edge biomarker identification method, takes an individual cancer patient as a dynamic network system, combines a dynamic network theory biomarker theory and a multi-modal evolutionary algorithm, performs hidden space search by using a restricted Boltzmann machine on the basis of an MMPDNB model, and is a new multi-modal PDENB identification model. Firstly, a PEN of the cancer individual patient is constructed. Then, an optimization objective function is designed. Finally, a multi-modal optimization algorithm is used to search for a PDENB set. The application can not only promote the researches on a mathematical model and an algorithm design of the PDENB identification problem, but also help to understand the individual heterogeneity of the cancer, and realize the early diagnosis and treatment of the cancer individual patient.

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

A method may include extracting a plurality of features for each cell depicted in an image. A biomarker identification model may be applied to determine, based on the features associated with each cell, whether the cell is associated with various biomarkers. A set of probabilities for each cell in the population of cells may be determined based on an output of the biomarker identification model. The set of probabilities may include, for each biomarker, a probability of a corresponding cell being associated with the biomarker. One or more subsets of cells, each of which corresponding to a different cellular phenotype, may be identified based on the set of probabilities associated with each cell. A feature set associated with each subset of cells may be identified as being indicative of a probability of a cell being associated with a corresponding phenotype. Related systems and computer program products are also provided.

The application belongs to the technical field of biological medicine, and particularly relates to a screening method of tumor metabolic markers. The screening method combines widely targeted metabolomics data and targeted metabolomics data with a biomarker identification and optimization algorithm based on function and feature importance recursive enhancement (BIO-FIRE) to screen tumor metabolic markers. The screening method can identify a stable metabolic marker combination in large-scale, multi-center research data to construct an efficient, accurate and generalizable gastric cancer metabolomics diagnosis scheme.