23 results about "Biomarker discovery" patented technology

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention discloses an early pancreatic cancer prediction and risk stratification system based on artificial intelligence, and belongs to the technical field of medical health data analysis and artificial intelligence. The system comprises a multi-omics data adaptive fusion module, a longitudinal health trajectory coding module, a biomarker combination discovery module, a risk prediction and dynamic layering module and a closed-loop feedback optimization module, and a data confidence index generated by the multi-omics fusion module directly affects the attention weight of longitudinal trajectory coding. Longitudinal track coding adopts a bidirectional long-short-term memory network to extract time sequence characteristics, a biomarker discovery module recognizes a synergistic marker combination through a Transform mechanism, a closed-loop feedback module dynamically adjusts parameters of each module according to a prediction result, and clinical verification shows that the prediction accuracy of the system reaches 85%, the I-stage diagnosis rate is improved by 60%, diseases are discovered 8-12 months in advance, and the diagnosis efficiency is improved. The method is obviously superior to the prior art.

The present invention relates to an aptamer specifically binding to cell-derived extracellular vesicles and a method for discovering an extracellular vesicle-specific biomarker using same. Specifically, the present invention relates to an aptamer specifically binding to cancer cell-derived extracellular vesicles, and a method for discovering an extracellular vesicle-specific biomarker using same. Unlike prior art requiring immobilization, a method for selecting an aptamer specifically binding to extracellular vesicles according to the present invention can select an aptamer specifically binding to extracellular vesicles that maintain a three-dimensional structure in an actual clinical environment. A biomarker discovery method using the aptamer selected through the selection method can be used to quickly and precisely identify unknown target proteins present in extracellular vesicles. The present invention can be efficiently applied to the fields of novel biomarker discovery, target protein verification for diagnosis, and aptamer-protein interaction-based drug development.

The invention relates to the field of material analytical chemistry and biochemistry, and provides a continuous batch pretreatment device and method for proteomics, glycoproteomics and phosphorylated proteomics samples. According to the continuous batch pretreatment device for the proteomics, glycoproteomics and phosphorylated proteomics samples, under the assistance of the filler, the whole process of protein denaturation, aggregation capture, enzymolysis and complete glycopeptide and phosphorylated peptide enrichment in biological samples can be achieved. Compared with a free solution method conventionally used in proteomics, the sample pretreatment method based on the device has the advantages that the sample treatment time can be effectively shortened, and continuous analysis of mass proteome, glycoprotein group and phosphorylated proteome of trace protein samples can be realized; the method is expected to be widely applied to the aspects of disease occurrence mechanisms, drug action mechanisms, clinical detection, biomarker discovery and the like.

The present application relates to the technical field of in vitro detection, in particular to a matrix material, a preparation method and application thereof. The present application provides a new matrix material for MALDI detection, which comprises MXene material and gold nanowires attached to the surface thereof. By using the high-sensitivity nanomaterial as the matrix material for MALDI detection, metabolites related to diseases (such as X-linked Alport syndrome) can be identified, which opens up a new way for biomarker discovery of diseases and may have important potential in future clinical applications.

The present disclosure relates generally to biomarker discovery and patient stratification, and more particularly to machine learning techniques for discovering related biomarkers using data collected as part of standard care (SoC), which may be used to identify related patient populations with therapeutic agents of known mechanisms of action (MoA). An exemplary method for predicting patient molecular analyte activity includes: training a first module of a machine learning model based on a plurality of medical images of a first queue; a second module to train the machine learning model based on one or more molecular analyte data sets obtained from a second queue; receiving a medical image from the patient; and predicting the activity of the molecular analyte from the medical image of the patient using the trained first and second modules of the machine learning model.

The present disclosure provides a method of EEG-based biomarker discovery and commercialization for personalized diagnosis and treatment of brain disorders. Further, the method may include receiving, using a communication device, a brain-wave data from a diagnostic device. Further, the brain-wave data corresponds to a graphical representation of an electrical activity of a brain of an individual. Further, the individual may be receiving at least one therapeutic for at least one brain disorder. Further, the method may include analyzing, using a processing device, the brain-wave data based on an artificial intelligence (AI) model. Further, the method may include generating, using the processing device, at least one output data based on the analyzing. Further, the method may include transmitting, using the communication device, the at least one output data to at least one device.

The present invention provides a SPARK-seq high-throughput platform for the identification and kinetics analysis of nucleic acid aptamers and target proteins thereof. By combining cell screening, CRISPR gene perturbation and single-cell multi-omics sequencing technology, the platform enables systematic identification of the interaction between thousands of nucleic acid aptamers and target proteins thereof in a single experiment; in addition, high-throughput screening of high-stability nucleic acid aptamers having "slow dissociation" characteristics is achieved on the basis of dissociation kinetics. The method provided by the present invention overcomes the limitations of the traditional technology of low throughput, difficulty in identifying targets having relatively low abundance, and inability to perform efficiently screening in natural cell environments, not only achieves large-scale and unbiased discovery of aptamer targets, but also can accurately screen high-stability aptamers having slow dissociation rates, providing more powerful molecular tools and new biomarker discovery pathways for tumor diagnosis, targeted therapy, and accurate medical treatment.

The present invention provides methods and systems using gas-phase separation with mass spectrometry analysis instead of liquid chromatography, thereby enabling faster peptide, proteome, and multi-omic analysis. Also provided are improved methods and software for data independent acquisition. One embodiment referred to as Direct Infusion-Shotgun Proteome Analysis (DI-SPA) used with data-independent acquisition mass spectrometry (DIA-MS), resulted in targeted quantification of over 500 proteins within minutes of MS data collection (˜3.5 proteins / second). Enabling fast, unbiased protein and proteome quantification without liquid chromatography, DI-SPA offers a new approach to boosting throughput critical to drug and biomarker discovery studies that require analysis of thousands of proteomes. This invention is also able to perform complex multi-omic analysis of proteomes, lipidomes, and metabolomes on a single platform.

The invention discloses a method for detecting metabolite containing phosphate radicals, which is characterized in that a mass spectrum label with stable and characteristic chlorine isotope distribution is introduced through a chlorination reagent derivatization strategy, so that a derivatization product can be subjected to rapid and high-specificity preliminary identification based on primary mass spectrum data. By combining with an automatic data mining program specially developed for this purpose, high-throughput and accurate screening and locking of potential phosphate radical-containing metabolites from massive and complex non-targeted metabonomics original data are realized, and the data analysis efficiency and reliability are greatly improved. The method is successfully applied to analysis of actual biological samples such as cholestasis disease models, efficient identification and accurate quantification of various key metabolites such as nucleotide and sugar phosphoric acid are achieved, disturbance of related metabolic pathways is disclosed, and a powerful innovative analysis tool is provided for disease mechanism research and biomarker discovery.

The invention discloses a feature selection and dimension reduction method and system for multi-omics data fusion, and the system comprises a multi-omics data collection and heterogeneous preprocessing module which is used for carrying out the omics specific preprocessing of genome, transcriptome, proteome and metabolome data; the cross-omics heterogeneous graph neural network fusion module is used for constructing a heterogeneous graph and carrying out information fusion by adopting a heterogeneous graph attention network; the feature selection module based on multi-task sparse learning is used for selecting a key feature subset from the fused features; a non-linear dimension reduction module based on an adversarial variational auto-encoder and used for performing dimension reduction on the key feature subset to a low-dimensional submerged space; and a result visualization and biological interpretation module. According to the method, efficient fusion and dimension reduction of heterogeneous and high-dimensional multi-omics data are realized, the accuracy of feature selection and the interpretability of results are improved, and an effective calculation tool is provided for disease typing and biomarker discovery.

The invention relates to the technical field of tumor molecular typing and biomarkers, and discloses a wide-period SCLC biomarker exploration method based on molecular typing, which comprises the following steps: carrying out molecular typing detection on tumor tissues of a patient, and collecting a peripheral blood sample; performing high-throughput immunochromatogram data acquisition on the peripheral blood sample; carrying out bioinformatics analysis on the high-dimensional data of the immune spectrum, and locking and quantifying a specific CD8 + T cell subset; calculating the frequency dynamic change of the specific CD8 + T cell subpopulation, and analyzing statistical relevance; and a set of biomarker standards and methodology is established. According to the invention, a set of complete biomarker standard and methodology is established by locking the specific CD8 + T cell subpopulation, calculating the dynamic frequency change of the specific CD8 + T cell subpopulation and analyzing the statistical relevance between the dynamic frequency change characteristics and clinical curative effect data of the patient, and can be used for judging or predicting the clinical curative effect of the wide-period SCLC patient.

This invention provides a SPARK-seq high-throughput platform for the identification and kinetic analysis of nucleic acid aptamers and their target proteins. This platform innovatively combines cell screening, CRISPR gene perturbation, and single-cell multi-omics sequencing technologies, enabling the systematic identification of thousands of nucleic acid aptamers and their target protein interactions in a single experiment. Simultaneously, it achieves, for the first time, high-throughput screening of highly stable nucleic acid aptamers with "slow dissociation" characteristics based on dissociation kinetics. The method provided by this invention overcomes the limitations of traditional techniques, such as low throughput, difficulty in identifying relatively low-abundance targets, and inability to perform efficient screening in natural cellular environments. It not only achieves large-scale, unbiased discovery of nucleic acid aptamer targets but also accurately screens for highly stable aptamers with slow dissociation rates, providing a more powerful molecular tool and a novel biomarker discovery pathway for tumor diagnosis, targeted therapy, and precision medicine.

The proposed AI drug discovery platform represents a new approach to pharmaceutical research and development, integrating cutting-edge artificial intelligence and machine learning technologies across the entire drug discovery pipeline. This comprehensive system leverages multi-modal data integration, quantum-classical hybrid computing, environmental factor analysis, and digital twin simulations to address the complexities of drug discovery and development. By combining advanced predictive modeling, generative design, and virtual clinical trial capabilities, the platform aims to significantly accelerate the identification and optimization of novel therapeutic compounds while improving safety and efficacy predictions. The system's modular architecture incorporates state-of-the-art techniques in protein design, biomarker discovery, and personalized medicine, enabling a more holistic and precise approach to drug development.

The present invention provides methods and systems using gas-phase separation with mass spectrometry analysis instead of liquid chromatography, thereby enabling faster peptide, proteome, and multi-omic analysis. Also provided are improved methods and software for data independent acquisition. One embodiment referred to as Direct Infusion—Shotgun Proteome Analysis (DI-SPA) used with data-independent acquisition mass spectrometry (DIA-MS), resulted in targeted quantification of over 500 proteins within minutes of MS data collection (˜3.5 proteins / second). Enabling fast, unbiased protein and proteome quantification without liquid chromatography, DI-SPA offers a new approach to boosting throughput critical to drug and biomarker discovery studies that require analysis of thousands of proteomes. This invention is also able to perform complex multi-omic analysis of proteomes, lipidomes, and metabolomes on a single platform.

The present disclosure in various aspects and embodiments provides systems and methods for evaluating or screening subjects for the presence or absence of colorectal neoplasia, such as colorectal cancer (CRC), colorectal adenoma (CRA), and / or colorectal advanced adenoma (CRAA), by metagenomic or multi-omic analysis of microbiome in biological samples, including fecal samples. In aspects, the present disclosure provides methods for generating machine learning models or signatures based on metagenomic or multi-omic analysis of the microbiome in biological samples, including fecal samples, to evaluate or screen subjects for the presence or absence of disorders, such as but not limited to CRC, CRA, and CRAA.

The invention discloses application of a biomarker in diagnosis / prediction of peritoneal relapse after a local progression stage gastric cancer operation, and particularly relates to a biomarker which is BUB1, CKS2, PCNA, CHEK1, NEK2, NCAPG2 or a combination of the BUB1, the CKS2, the PCNA, the CHEK1, the NEK2 and the NCAPG2. Through the systematic and comprehensive biomarker discovery and multi-sample verification method, a six-genome and risk stratification model is determined to detect the risk of peritoneal relapse after a high-risk gastric cancer operation, and if the risk is discovered as early as possible, information decision can be provided for clinic, and the treatment result of a patient can be improved.

Systems and methods are directed towards a web application for diagnosing cancer using multimodal artificial intelligence analysis. A web-based artificial intelligence system for multi-modal cancer diagnosis comprises a web application interface that receives uploads of patient data including biopsy images, electronic health records, and omics data; an ensemble of deep learning models including at least a first convolutional neural network and a second convolutional neural network that process the biopsy images at multiple magnification levels to generate cancer grade predictions; a multimodal data integration module that combines the cancer grade predictions with patient demographic information and clinical data from the electronic health records and omics data; and a visualization component that displays the cancer grade predictions as interactive charts with color-coded probability distributions for different cancer severity levels.

The invention relates to the technical field of in-vitro detection, in particular to a matrix material as well as a preparation method and application thereof. The invention provides a novel matrix material for MALDI (matrix-assisted laser desorption ionization) detection. The matrix material comprises an MXene material and a gold nanowire attached to the surface of the MXene material. The high-sensitivity nano material is used as a matrix material for MALDI detection, metabolites related to diseases (such as X-linked Alport syndrome) can be identified, a new way is opened up for biomarker discovery of the diseases by the metabolites, and the high-sensitivity nano material possibly has important potential in future clinical application.

The invention discloses a multi-omics joint detection method based on gradient nucleic acid coding and capillary electrophoresis and a system for carrying out the method. The multi-omics joint detection method based on gradient nucleic acid coding and capillary electrophoresis comprises the following steps: designing a plurality of oligonucleotide pairs with different lengths and sequences; preparing a marking probe pair; reacting and hybridizing to form an oligonucleotide pair; generating a DNA template corresponding to the object to be detected; dNA amplification products with markers and different lengths are generated; preparing a capillary electrophoresis spectrogram; identifying a corresponding to-be-detected object type; and / or performing quantitative analysis on a corresponding to-be-detected object. The method has a wide application prospect in life science researches such as clinical diagnosis, biomarker discovery, protein, gene and metabonomics.

The disclosure relates to devices, solutions and methods for collecting and processing samples of bodily fluids containing cells (as well as embodiments for the collection, and processing and / or analysis of other fluids including toxic and / or hazardous substances / fluids). In addition, the disclosure relates generally to function genomic studies and to the isolation and preservation of cells from saliva and other bodily fluids (e.g., urine), for cellular analysis. With respect to devices for collection of bodily fluids, some embodiments include two mating bodies, a cap and a tube (for example), where, in some embodiments, the cap includes a closed interior space for holding a sample preservative solution and mates with the tube to constitute the (closed) sample collection device. Upon mating, the preservation solution flows into the closed interior space to preserve cells in the bodily fluid. The tube is configured to receive a donor sample of bodily fluid (e.g., saliva, urine), which can then be subjected to processing to extract a plurality of cells. The plurality of cells can be further processed to isolate one and / or another cell type therefrom. The plurality of cells, as well as the isolated cell type(s), can be analyzed for functional genomic and epigenetic studies, as well as biomarker discovery.

This invention discloses a method for the simultaneous quantitative detection of multiple metabolites in biological samples. The method employs chromatographic tandem mass spectrometry (GC-MS) to detect the biological samples, comprising the following steps: (1) optimizing mass spectrometry detection parameters to obtain the optimal mass spectrometry detection parameters for each metabolite; (2) applying the optimal mass spectrometry detection parameters to detect the biological samples and classifying each metabolite; (3) optimizing and obtaining the final CE value for each metabolite; (4) using the final CE value corresponding to each metabolite to detect the biological sample to be tested, and using the internal standard method to perform quantitative analysis of each metabolite in the biological sample to be tested, thereby obtaining the content of each metabolite in the biological sample to be tested. Furthermore, an absolute quantitative analysis method based on eliminating interference from natural isotopes in metabolites is also provided. The method of this invention has high throughput, high accuracy, simple operation, high experimental reproducibility and operability, and can be applied to clinical pathophysiological research and biomarker discovery.

The application discloses an SDOCT modal DR and RVO edema biomarker discovery method. The method first trains a deep learning model using collected SDOCT data to automatically classify SDOCT images of two different retinal diseases. Then, using feature visualization technology, the attention area of the deep model in the classification decision is displayed in the form of a heat map, and the distribution of the attention area of the deep model is counted, and accordingly, potential new biomarkers are proposed to distinguish the two diseases, and the new biomarkers include a group of quantitative features such as retinal outer layer (outer limiting membrane (ELM), elliptical zone (EZ) and retinal pigment epithelium (RPE)) fracture rate, blocking rate and average gray scale. Finally, statistical analysis is performed on these quantitative features, and features with strong statistical differences (p value less than 0.001) are used as the final newly discovered biomarkers.