250 results about "Expression data" patented technology

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention relates to the technical field of artificial intelligence interaction, in particular to a full-life-cycle AI accompanying agent system and method.The full-life-cycle AI accompanying agent system comprises a core engine layer, a business service layer, a basic tool layer, a data layer and an interface layer, and the full-life-cycle AI accompanying agent method comprises the steps that multi-modal data, including biological signals, behavior data, voice data, expression data and environment data, of a user are collected; constructing a user digital twinborn model, and updating model parameters through a weighted learning algorithm; identifying a user cognition development stage, and dynamically adjusting interaction complexity and an expression mode; emotional connection is established, and resonance feedback is generated through multi-mode emotional recognition and memory retrieval; according to the method, interaction memory is stored, cross-stage retrieval is realized, data management is performed by adopting a hierarchical memory architecture, and accurate judgment of a cognitive stage is realized by synchronously collecting multi-dimensional data such as behavior tracks, language texts and biological signals of a user and combining a development psychology theoretical model to construct a feature association map.

The invention discloses an old people abnormal expression recognition method based on gradient covariance analysis. The method comprises the steps of old people expression data set construction, time sequence optical flow feature-based facial expression region screening, abnormal expression enhancement loss estimation, gradient covariance-based facial expression region feature enhancement, micro-expression model training and micro-expression model testing. Aiming at the problems that the expression movement of the elderly is not obvious and the abnormal expression is easy to neglect, the facial expression key region is positioned by using the time sequence optical flow feature, the abnormal expression is introduced to enhance the loss so as to punish the leak detection condition, the back propagation gradient of the loss and the covariance thereof are calculated, and the region with larger covariance has more obvious feature change, so that the detection accuracy is improved. According to the method, facial expression region features based on gradient covariance are fully considered, key region analysis is enhanced, and the accuracy of abnormal expression recognition of the old people is effectively improved.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention relates to the technical field of biological information, in particular to a brain glioma microenvironment formation key molecular mechanism analysis method, which comprises the following steps: calling expression data to analyze a cell marker sequence to construct a segmentation interval, calculating a candidate factor expression direction to judge trend consistency, identifying expression aggregation difference to construct a split node section, and constructing a subsection; according to the method, partitions are constructed on the basis of marker expression syn-position, judgment is carried out in combination with candidate factor expression directions and marker trends, factor collaboration features are constructed according to the number of trends consistent times, and the semantic sorting information is generated by analyzing the channel change trends to generate drift scores, evaluating multi-label output stability screening key factors and analyzing literature word order positions. Identifying and expressing an aggregation and split structure, guiding a functional pathway to perform trend analysis in a scoring interval and construct a dynamic trajectory, judging label stability and empowerment according to pathway scoring difference, and determining a factor semantic position in combination with a literature word order structure to realize integrated support of regulation and control information and literature evidence.

The invention provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.

The invention relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

The invention discloses an RNA (Ribonucleic Acid) binding protein enrichment analysis method based on single cell expression profile data, which comprises the following steps of: writing an interactive online Web application program by using Shiny, carrying a data visualization tool, and using an RBP (Reactive Binding Protein)-gene database as a database; the method is specially designed for single cell data, different single cell data can be integrated for enrichment analysis, and expression profiles and RBP dynamic changes in single cells can be effectively captured and analyzed; rBP enrichment analysis is customized for single cell data, a single group and multiple groups of single cell expression data are preprocessed, including normalization processing, and a statistical model is used to evaluate enrichment significance at each time point, so that the analysis accuracy and biological correlation are improved; a dynamic visualization tool is provided, the dynamic visualization tool comprises a customizable network diagram, an interactive bar diagram and detailed table display, and a user can adjust a plurality of parameters of a view according to needs and directly download images and data from an interface.

The present disclosure relates to predicting genomic instability status in biological samples using machine learning techniques with comprehensive genomic and immune profiling (CGIP) data. Particularly, aspects are directed towards performing a genomic instability test on a biological sample. Then, multi-omics data for the subject are obtained by DNA sequencing and RNA sequencing assays, including genomic alteration data for a first set of genes and expression data for a second set of immune genes. The multi-omics data are input into a machine learning model having a tree-based architecture, which is configured to analyze features by traversing paths from root nodes to terminal nodes in each tree based on values generated from the data. The model predicts a genomic instability status, which is then provided via a user interface notification or as part of a testing report.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

A disease marker expression regulatory mechanism prediction method comprises the following steps: acquiring gene expression data, performing quality control and standardization processing on the gene expression data to obtain standardized gene expression data, identifying differentially expressed genes as candidate markers, and predicting a disease marker expression regulatory mechanism. A transcriptional regulation and control network of the candidate markers is constructed, transcription factor data are generated, a multi-layer neural network model is established through a four-quadrant neural network model, and the four-quadrant neural network model comprises a contribution value calculation layer, a statistical test layer, a quadrant division layer, a feature integration layer and a comprehensive output module. The four-quadrant neural network model adopts an adaptive quadrant division optimization equation set to carry out feature analysis and prediction on the candidate markers; compared with the prior art, the method is remarkably improved in the aspects of feature integration, dynamic modeling, self-adaptive optimization and the like, and important theoretical basis and technical support are provided for disease diagnosis, prognosis evaluation and treatment strategy formulation.

The invention discloses a multi-technology combined medication analysis platform of an oligonucleotide drug, which relates to the technical field of drug analysis and is technically characterized by comprising an oligonucleotide drug delivery technology module, a liposome, a polymer nanoparticle or an exosome is selected as a delivery carrier, the surface of the delivery carrier is modified with hydrophilic polyethylene glycol or a targeting ligand, and the oligonucleotide drug delivery technology module is connected with the oligonucleotide drug delivery module. The performance of the material is represented by dynamic light scattering and a transmission electron microscope; the multi-technology platform analysis module integrates RT-qPCR, LC-FL, LC-MS / MS and LC-HRMS technologies, is respectively used for target gene expression quantification, drug distribution tracking, drug principal component quantification and metabolite structure analysis, establishes a cross validation rule, and requires a correlation coefficient R2gt of an LC-MS / MS quantitative result and RT-qPCR expression data; 0.95%, 0.95%; the pharmacokinetic evaluation module constructs a PBPK model to predict human pharmacokinetic parameters, and draws a drug metabolism network diagram in combination with an LC-HRMS metabolite identification result; by integrating various advanced analysis technologies, the problems of low sensitivity, poor specificity and difficult metabolite analysis in the prior art are solved.

The invention discloses a mathematical formula identification coding method, and particularly relates to the technical field of formula coding. The method comprises the following steps: acquiring mathematical formula image data to be identified, and performing symbol boundary extraction and preprocessing to generate symbol feature expression data; and performing visual spatial layout analysis and symbol type semantic classification based on the symbol feature expression data to generate formula layout structure data and symbol semantic classification data. And through graph structure analysis based on a topological relation, determining an inter-symbol topological relation of the mathematical formula, and generating symbol topological relation data. And deriving a dimension constraint relationship and an operator dependency relationship between symbols through a mathematical meta-knowledge mining technology, and generating mathematical meta-knowledge constraint data. Initial mathematical formula structure expression data is generated through decoding of structure and semantic constraint fusion, and a formula coding sequence is generated through formula consistency verification and semantic constraint reconstruction. According to the invention, the accuracy and efficiency of mathematical formula identification can be effectively improved.

The invention discloses a method for annotating a vesicle cell subset based on monovesicle membrane proteomics, and belongs to the technical field of vesicle cell annotation. The method comprises the following steps: 1, carrying out exosome collection on a plasma sample to obtain a sample; 2, coding and labeling the antibody probe, and adding a protein tag and a labeled DNA sequence into the antibody probe to obtain a labeled antibody probe; 3, preparing a corresponding combined product; 4, adding a labeled antibody probe into the sample to obtain an exosome compound; step 5, capturing an exosome conjugate through CTB; 6, adding the binding product into the exosome conjugate to generate a sequence; 7, constructing a sequence library and sequencing to obtain a sequencing sequence; 8, removing a sequencing sequence with low expression quantity to obtain an exosome expression data matrix, and standardizing the exosome expression data matrix; 9, carrying out clustering analysis to find out an exosome core subgroup; and step 10, determining the source of the exosome core subgroup, and annotating the single vesicle subgroup.

The invention discloses an empty transgene expression filling method based on a conditional variation auto-encoder, which comprises the following steps: designing a unified framework for conjoint analysis of single cell transcriptome data and spatial transcriptome data, obtaining single cell transcriptome sequencing expression profile data, spatial transcriptome expression data and a COVET matrix used for coding local neighborhood covariance in a tissue; projecting single cell transcriptome sequencing expression profile data and spatial transcriptome expression data into a shared potential space through an attention enhancement encoder to obtain potential variables; and decoding gene expression from the potential variables by using a decoder network, filling up missing gene expression information in spatial data, predicting a COVET matrix of single cell transcriptome data, and deducing a spatial context. According to the method, gene expression and spatial information can be coded at the same time, so that spatial context prediction of single cell data and filling of missing genes in spatial data are realized.

The invention discloses a microfluidic multi-organ tumor chip targeted drug test AIGC system, and relates to the technical field of medical drug test information processing. The microfluidic multi-organ tumor chip targeted drug test AIGC system comprises a multi-omics data acquisition module for acquiring and preprocessing disturbance response data and dynamic expression data; the disturbance response pre-screening module is used for carrying out strength evaluation on drug action strength and reconstructing channel mapping; the characteristic space reconstruction module is used for performing action analysis on the whole medicine action state and dynamically adjusting a liquid medicine blending rule; the drug effect prediction module is used for predicting and evaluating the response degree of the organoid under the intervention of the specific drug and deducing the individualized drug effect intensity; and the feature augmentation self-closed loop module constructs a negative supervision signal and dynamically corrects a disturbance perception and feature embedding path. The problems that current multi-omics data is high in dimension, large in redundancy and large in noise, key features are difficult to extract under limited samples, and model generalization is difficult to maintain are solved.

The invention discloses a cognition training adjustment method, device and system based on physiology and performance. The method comprises the steps that performance data and physiological data of a user when the user participates in a target cognition game are obtained; inputting the expression data and the physiological data into a game adaptation analysis model to obtain an analysis result; if the analysis result is that the game is not matched, the target cognitive game is updated, and the step of obtaining the performance data and the physiological data when the user participates in the target cognitive game is executed again according to the updated target cognitive game; and if the analysis result is game adaptation, generating a cognitive training report according to the performance data, the physiological data and the analysis result. When the user participates in the target cognitive game, the performance data and the physiological data are obtained, whether the target cognitive game needs to be updated or not is determined based on the analysis result obtained by the performance data and the physiological data, the best challenge can be continuously provided, and the cognitive training effect is improved.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

The embodiment of the invention provides a sepsis prediction method, electronic equipment and a medium, and the method comprises the steps: inputting expression data corresponding to a plurality of different target genes of a patient into a sepsis prediction model, and obtaining a sepsis prediction result of the patient; wherein the sepsis prediction model is obtained through the following steps that a plurality of first training samples are obtained, and the first training samples comprise sample expression data of all target genes; for each first training sample, inputting the first training sample into a pre-trained analytical model to obtain a training prediction result of the first training sample and contribution relationship information between each piece of sample expression data in the first training sample and the training prediction result; and adjusting the initial sepsis prediction model according to the multiple pieces of contribution relationship information corresponding to the first training samples to obtain the sepsis prediction model. According to the embodiment of the invention, the accuracy and reliability of sepsis prediction can be improved.

The invention relates to the technical field of child emotion recognition, in particular to an emotion calculation method and device for child expressions, and the method comprises the steps: collecting and marking the multi-modal dynamic expression data of a plurality of Asian children, and constructing an enhanced mixed expression database according to the multi-modal dynamic expression data of the Asian children; performing iterative training on a pre-constructed deep convolutional neural network model by using the enhanced mixed expression database until a preset iterative period is reached, so as to obtain a deep time sequence emotion calculation model; and inputting the multi-modal dynamic expression of the child to be recognized into the depth time sequence emotion calculation model to calculate the current emotion of the child. Therefore, the problems that an existing child emotion recognition model is mostly based on European and American adult database training and mostly adopts single-frame static image analysis, and the complete dynamic process of expressions cannot be captured, so that the accuracy is remarkably reduced, the model robustness is poor, and effective application in real and continuous interaction scenes is difficult are solved.

The present invention relates to a method for determining the IC50 value of the toxicity of a selenium compound to cancer cells based on gene expression. Specifically, the method comprises the steps of: (1) performing dimensionality reduction processing on cancer cell gene expression data using an FCBF algorithm to obtain a cancer cell gene expression dataset F' after dimensionality reduction; (2) dividing the cancer cell gene expression dataset F' after dimensionality reduction obtained in step (1) into an F' training sample subset and an F' test sample subset; (3) performing a Gauss-Newton iterative algorithm on the F' training sample subset and the IC50 values ​​of the toxicity of selenium compounds to different cancer cells to obtain a determination model for determining the IC50 value of the toxicity of selenium compounds to cancer cells based on cancer cell gene expression; using the determination model, determining the IC50 value of the toxicity of selenium compounds to cancer cells according to the gene expression of cancer cells. The method of the present invention can quickly and accurately determine the IC50 value of the toxicity of selenium compounds to cancer cells based on gene expression data.

The invention relates to the technical field of data analysis and processing, and discloses an intelligent evaluation method for a project road presentation, which comprises the following steps of: acquiring multi-modal data of a road presentation video in real time, including speech text, voice audio, expression video and PPT content, and analyzing and processing the multi-modal data through a multi-modal fusion technology to ensure feature alignment; generating a core challenge question set based on the analysis data, and verifying the payment willingness deviation ratio of a target user for commercial logic, technical implementation and a financial model; executing three rounds of progressive questioning; when the multi-modal data analysis of the project road performance is carried out, the analysis deviation of voice, text and visual data can be recognized in real time by constructing a collaborative alignment mechanism of multi-modal feature fusion, and the high conformity of inquiry problem generation and real commercial risks is ensured; and meanwhile, a confidence-driven redundancy check process is established, expression data is actively fused to correct and output when the voice recognition credibility is insufficient, and the accuracy of dynamic questioning and the key vulnerability capturing capability are improved.

The present disclosure provides a landmark feature determination method and device, electronic equipment and storage medium. The landmark feature determination method comprises: obtaining target expression data, wherein the target expression data is used to represent the expression amount of each candidate feature in different cells; the average expression amount and expression proportion of each candidate feature in different cell types are counted; for each candidate feature, the first inter-class comparison data is determined according to the average expression amount of the candidate feature in the target cell type and the average expression amount of the candidate feature in the non-target cell type; for each candidate feature, the second inter-class comparison data is determined according to the expression proportion of the candidate feature in the target cell type and the expression proportion of the candidate feature in the non-target cell type; and the landmark feature of the target cell type is determined according to the first inter-class comparison data and the second inter-class comparison data. The embodiment of the present application can solve the problem of landmark feature specificity.

This application discloses a method, system, and device for analyzing the state of the tumor microenvironment based on medical images, relating to the field of medical image processing technology. First, by acquiring medical images and gene expression data of related tissues within those images, the medical images are preprocessed to obtain image patches, and semantic features of these patches are extracted. An algorithm is then used to calculate a predefined immune resistance mechanism that matches the gene expression data as the dominant immune resistance mechanism. This dominant immune resistance mechanism is then set as a supervisory label for the model, and a prediction model is constructed. The model is trained using a large amount of data, and the medical image to be analyzed is input into the trained prediction model to obtain the predicted category of the resistance mechanism. This application can accurately predict the resistance mechanism of pathological tissues based on medical images and their related gene expression data.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The invention belongs to the technical field of biomedical engineering, and discloses a gene regulatory network optimization method based on a diffusion model, which comprises the following steps: acquiring gene data of cells under a steady state condition, and constructing a gene expression matrix according to the gene data; injecting Gaussian noise into the gene expression matrix based on a diffusion model method to generate a series of noisy data sequences; performing noise estimation and structure estimation on the noisy data sequence by a noise estimator and a structure estimator based on a gene regulation and control network, and performing reverse denoising processing according to the noise estimation and the structure estimation to obtain gene structure estimation after reverse denoising; performing structure optimization on the gene structure estimation after reverse denoising by adopting an acyclic constraint function and a regularization substitution method; and outputting the optimized gene structure estimation. According to the method, the regulation and control relation between the genes is accurately recognized from high-dimensional gene expression data, and the modeling precision of the regulation and control relation between the genes is improved.

The invention relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.