326 results about "Expression data" patented technology

The invention provides a cell type and cell abundance identification method and system based on cross-modal training, relates to the field of image processing, and aims to solve the problems that the existing identification technology mostly depends on non-standard factors such as manual labeling position annotation information and the like, abundant morphological modes in a tissue pathological image are not fully utilized, and the identification accuracy is poor. And the identification reliability and accuracy are influenced. The method comprises the following steps: acquiring spatial transcriptomics data matched with pathological image-gene expression, and preprocessing the spatial transcriptomics data to obtain high-expression gene expression data, local image blocks, cell types and abundance tags; constructing a cross-modal joint representation learning model, and inputting high-expression gene expression data and local image blocks into the model for training; and predicting a to-be-predicted histological image based on the trained model. According to the method, the problems in the prior art are solved, the capability of predicting the cell abundance from the histological image is improved, and the spatial distribution of fine-grained cell types is fully revealed.

The invention discloses an emotion recognition method based on electroencephalogram (EEG) and expression data, which combines fusion analysis of electroencephalogram (EEG) signals and facial expression data, and provides an innovative solution for the complexity and accuracy of emotion recognition. The method comprises the following specific implementation steps: 1, finding an optimal frame in an expression video by using multi-instance learning, extracting global visual features by adopting Swin Transform, and realizing dimension reduction and balance representation of cross-modal information through a cross attention mechanism; 2, preprocessing the electroencephalogram signal by using band-pass filtering and a fast ICA algorithm, and extracting effective features by using PSD; and 3, designing a fusion module based on Transform, combining the characteristics of the two modes, and giving full play to the complementary advantage of the modes. Experimental results show that 94.85% of classification accuracy is achieved in four types of emotion classification tasks, and compared with an existing method, the accuracy is remarkably improved.

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 discloses a spatial transcriptomics cell clustering method based on multi-scale contrast learning. The method comprises the following steps: S1, carrying out data preprocessing; s2, performing graph construction by using the processed data; s3, performing data enhancement; s4, extracting cell gene expression information by using GCN; s5, enriching node information by using a ContraNorm layer; and S6, further learning information by using multi-scale image comparison learning, and performing biological analysis by using final information obtained by learning. According to the method, computer-aided cell type analysis is utilized, a large amount of high-quality data is not needed, only gene expression data and cell space position information data are utilized, which cells belong to the same category can be predicted, and more effective information is provided to help researchers to identify the onset of cancer and the development of diseases.

The invention belongs to the technical field of medical pathological images, and particularly relates to a multi-instance multi-task learning pan cancer biomarker prediction method and system, and the method comprises the steps: obtaining a full-view digital pathological image and pan cancer biomarker expression data of a solid tumor patient; performing preprocessing and feature extraction based on the full-view digital pathological image to obtain a pathological feature data set; based on the pathological feature data set, inputting the pathological feature data set into a twin multi-example full convolutional network model for training to obtain pathological low-dimensional features, and calculating an attention score corresponding to each pathological feature in combination with a linear attention mechanism; and in combination with each pathologic low-dimensional feature and the corresponding attention score, calculating to obtain a pathologic depth feature, and inputting the pathologic depth feature into a multi-task classification prediction model for classification prediction to obtain a prediction result of each biomarker in the pan cancer.

The invention provides a crop whole genome prediction method and system based on machine learning, and the method comprises the steps: firstly obtaining a gene expression data set of a target crop in a plurality of growth cycles, the gene expression data set containing a plurality of gene sequence samples composed of genetic markers and phenotypic character data; performing feature extraction on the gene expression data set to obtain gene association features and growth character features, performing fusion prediction on the gene association features and the growth character features by using a preset machine learning model to generate fusion prediction features, and determining a whole genome prediction result according to the fusion prediction features. The whole genome prediction result can indicate character expression trends of crops in different environments, and finally, an adaptability optimization strategy is generated based on the whole genome prediction result and fed back to a crop cultivation system, cultivation parameter adjustment is triggered, crop cultivation precision and intelligence are achieved, and the cultivation efficiency and crop adaptability are improved.

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 discloses an interpretable cancer survival prediction method based on multi-modal data fusion, and the method comprises the following steps: carrying out the preprocessing of a full-section pathological image, and obtaining an image feature matrix; preprocessing a pathological report text corresponding to the pathological image, and constructing a text feature matrix; preprocessing the high-dimensional gene expression data of the patient to generate a plurality of survival-related gene modules and feature vectors thereof; performing dynamic fusion on the obtained multi-modal features through a self-adaptive multi-modal expert hybrid module to obtain final fusion feature representation; and training a deep learning model by using fusion feature representation in combination with a negative log-likelihood loss function and a Cox proportional risk model, and performing prognosis analysis on the cancer patient. According to the explainable cancer survival prediction method based on multi-modal data fusion, the prediction accuracy is improved, good model interpretability is achieved, and biomarkers closely related to cancer prognosis can be automatically recognized.

The invention discloses a variable polyadenylation dynamic analysis method and device. The method comprises the following steps: acquiring initial space transcriptome data and an initial space bright field image; performing first preprocessing on the initial space transcriptome data to obtain target space transcriptome data; performing second preprocessing on the initial space bright field image to obtain a target space bright field image; performing data analysis and integration processing to obtain spatial variable polyadenylation data, spatial gene expression data and spatial cell distribution dynamic data; constructing a gene expression regulation network; generating an integrated analysis result; performing space-time analysis to obtain a space-time analysis result; performing trajectory analysis to obtain a trajectory analysis result; performing disease process analysis to obtain a disease process analysis result; and generating a variable poly-adenosine acidification dynamic analysis result. According to the invention, dynamic analysis of variable polyadenylation is realized, the accuracy is improved, and multi-angle biological understanding is provided. The method can be applied to the technical field of gene analysis.

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 discloses a colorectal cancer prognosis model construction method based on fatty acid metabolism related genes, and belongs to the technical field of biomedicine, and the colorectal cancer prognosis model construction method comprises the following steps: S1, obtaining transcriptome data of a colorectal cancer diagnosis individual from a TCGA database; s2, performing difference analysis on fatty acid metabolism related gene expression data of the colorectal cancer tissue and the normal tissue, and screening out differentially expressed genes; s3, performing single-factor Cox regression analysis on the differentially expressed genes, and screening out genes related to colorectal cancer prognosis; according to the construction method of the colorectal cancer prognosis model based on the fatty acid metabolism related genes, provided by the invention, the tumor immunotherapy reactivity is evaluated by introducing TIDE scores, and the prior art is improved, so that the immune escape potential and the immunotherapy effect of tumor subtypes are analyzed. The prognosis of a colorectal cancer patient is effectively predicted, and a new molecular marker is provided for personalized treatment of the colorectal cancer.

The invention discloses a miRNA and disease relation prediction system and method based on causal feature selection. According to the system, the accuracy of miRNA and disease relation prediction is improved through a plurality of modules such as data acquisition, negative sample selection, feature selection and causal analysis, model training and evaluation and the like. The data acquisition module is used for acquiring miRNA expression data and disease characteristics from a biomedical database; the negative sample selection module is used for grouping unknown miRNA-disease relationships through k-means clustering and randomly selecting negative samples; the feature selection module adopts a causal feature selection algorithm to construct a Markov blanket so as to screen out miRNA features directly related to disease states, and redundancy is reduced; the model training module uses a single machine learning algorithm for training, and optimizes the model performance through cross validation; and finally, the evaluation module evaluates the model effect through indexes such as AUROC and AUPR. According to the method, the accuracy of miRNA and disease relation prediction is remarkably improved, and important support is provided for medical research.

The invention discloses a schizophrenia risk structure variation identification and function evaluation method based on three-generation sequencing, and relates to the field of molecular biology, whole genome sequencing is performed on peripheral blood DNA of a patient through three-generation sequencing, multi-tool joint detection is adopted, multi-sample results are integrated, and a high-confidence SV data set is generated; through cross-queue comparison, the patient specific SV is screened, and the high-risk potential pathogenic SV is identified in combination with an SV priority ordering tool SVJudge. By combining transcription factor binding analysis, SCZ drug target data and histocyte specific expression data, the influence of SV on gene regulation is evaluated, and the potential action mechanism of SV in SCZ is analyzed. The SCZ risk gene is screened based on SVJudge scoring and patient carrying conditions, the genetic risk and pathogenic mechanism of the SCZ are analyzed and verified through pathway enrichment, a protein interaction network and a functional module, and a new technical means and theoretical basis are provided for genetic research of the SCZ.

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 prediction model construction method for rheumatoid arthritis accompanied with brain cognitive impairment, and belongs to the technical field of medical assistance, and the method specifically comprises the steps: synchronously collecting multi-source dynamic behavior data when a patient executes standardized joint activity training each time; vector superposition comparison under a three-dimensional coordinate system is carried out on the joint motion trajectory data, and the trajectory offset degree of each motion phase point is calculated; fractal dimension calculation is carried out on the moving path topological structure data, and path complexity is quantized; performing feature matrix construction on the facial micro-expression data, and calculating covariance structure change of an expression parameter matrix; and performing time domain alignment and causal analysis on the spatio-temporal trajectory offset event, the path topology degradation characteristic and the expression parameter variation signal, and when the three types of abnormalities present time coupling in two continuous training periods and meet a preset condition, generating early warning of cognitive impairment.

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 early diagnosis and treatment system for children with autism based on facial representation multi-modal fusion. A system collects a face video when a child watches a specific video (7 minutes, 08 seconds), first 2000 frames of images are extracted as input, expression data are extracted by adopting an improved contrast language-image pre-training model, sight line data are obtained in combination with GazeMobileNet, and the expression data are displayed on a display screen. Time sequence feature extraction and classification are carried out through a multi-mode fusion network (including a preliminary memory module, a key extraction module and a hidden learning module) of a multi-valve memory mechanism. In the intervention stage, the system provides intervention games such as whack-a-mole, pattern recognition and the like to train reaction, cognition, coordination and language abilities, and an evaluation report is generated according to training data such as reaction time and the like. The system integrates data acquisition, feature learning, multi-modal classification and intervention training, improves the diagnosis accuracy and the intervention effect, and has the characteristics of simplicity and convenience in operation and high universality.

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.