673 results about "Omics" patented technology

The embodiment of the invention discloses a life omics research method and device based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a project according to the intelligent interaction between a user and a system, and generating standardized data; and meanwhile, carrying out transfer learning on the large language model to construct a life science large language model. And according to the standardized data, decomposing a research target by adopting a life science big language model in cooperation with an intelligent agent, and generating an analysis plan. And based on the analysis plan, the intelligent agent is scheduled through the coordinator, and a hierarchical task is generated. And calling a multi-omics analysis tool by the intelligent agent to calculate and analyze the grading task, and outputting an analysis result. And based on data features, integrating analysis results through an integrated advanced model, and outputting a final report to complete the research of life omics. According to the embodiment of the invention, full-process automation and natural language interaction are realized, the standardization of data analysis and the repeatability of results are ensured, the research efficiency is remarkably improved, and the technical threshold is reduced.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention discloses a prostate cancer endocrine therapy drug resistance prediction system based on multi-model artificial intelligence. The prostate cancer endocrine therapy drug resistance prediction system comprises a data acquisition and preprocessing module, a single omics prediction module, a multi-modal feature integration module, a model training module and a prediction result output module. Multi-omics data of a patient is collected and preprocessed, data representations of omics data of different labels are generated by using priori knowledge, and an optimal single-omics prediction model is matched for the omics data of different labels based on the data representations after knowledge enhancement. And constructing a multi-omics prediction model of the prostatic cancer endocrine treatment drug resistance by using the optimal single-omics prediction model corresponding to the different label medical data by adopting Stacking, predicting the drug resistance risk of the patient within a preset time, outputting an early warning, and adjusting the treatment scheme of the patient according to the drug resistance risk. According to the method, the multi-omics data are integrated, the accurate multi-omics prediction model for the drug resistance of the endocrine therapy of the prostate cancer is constructed, and the accuracy and clinical applicability of drug resistance prediction are improved.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention relates to a method for acquiring a biomarker for assisting CRLM early diagnosis. The method comprises the following steps: acquiring sequencing data, clinical information and metabonomics characteristics of a CRC sample; evaluating and determining a plurality of differentiated machine learning models; performing classification according to the determined machine learning model, and constructing a CRLM biomarker prediction model; screening candidate biomarkers according to the prediction model; and verifying the candidate biomarker at least according to the tissue slice and the serum sample, and determining a final biomarker.

Disclosed herein is a system for detection of a molecular community in a molecular network, the system comprising an information handling system that comprises at least one processor; and a non-transitory storage medium storing instructions readable and executable by the processor to perform a method comprising receiving molecular data from an analytical device and / or a data repository; generating an unpruned molecular network from the molecular data; treating the unpruned network with a community detection algorithm to form one or more molecular communities; parsing the molecular communities to prevent the formation of singletons; and identifying at least one molecule present in the molecular community by comparing the molecular community with reference spectra from a library of spectra or by using a molecular structure prediction tool.

The invention provides a spatial omics data completion method and system based on a variational graph auto-encoder, and relates to the technical field of bioinformatics. Extracting partial common genes in the data pair to obtain a feature matrix; based on the spatial position information, obtaining a first sub-adjacency matrix of cells in the spatial transcriptomics sequencing data; respectively obtaining a second sub-adjacency matrix of the cells in the single-cell RNA sequencing data and a third sub-adjacency matrix of the cells in the data pair based on gene expression similarity; combining the first sub-adjacency matrix, the second sub-adjacency matrix and the third sub-adjacency matrix to obtain a final adjacency matrix; and inputting the feature matrix and the final adjacent matrix into a pre-trained variational graph auto-encoder network to complete the deletion gene of the space transcriptomics. According to the method, the position information and gene expression characteristics between idle data cells can be effectively utilized, errors can be reduced, and meanwhile, the similarity between complementation genes and true values can be improved.

The invention relates to the field of forensic medicine, and discloses a method and system for identifying smoking behaviors and application of the method and system. The construction method comprises the following steps: respectively obtaining saliva and excrement of a smoking object and a non-smoking object; performing metagenome sequencing to obtain original microbiome data; after processing the original microbiome data, extracting multi-omics feature information, including species information and gene function annotation information; based on a statistical analysis method, carrying out diversity analysis and screening microbial markers and functional genes significantly related to smoking behaviors; constructing a training set and a test set, and training a machine learning model and model evaluation by taking the microbial markers and / or the functional genes as input features; and performing smoking behavior identification on a target sample by using the model, and outputting a prediction result. The accuracy rate of model identification reaches 0.7966, the sensitivity is 0.8750, the specificity is 0.7037, and the method can be applied to health assessment and judicial expertise.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The invention relates to the technical field of gene sequencing, in particular to a silkworm gene sequencing path optimization system and method based on a hybrid parallel genetic algorithm. According to the technical scheme, the method comprises the steps of data preprocessing and feature modeling, hybrid parallel genetic algorithm optimization, dynamic path planning and resource allocation and multi-omics verification and result output. Reference is provided for assembling path planning by recognizing the repeated area, local optimum is avoided by means of the hybrid parallel genetic algorithm, efficient assembling of the high-complexity repeated area is achieved, standardization processing is carried out on data, errors caused by data differences are reduced, and the accuracy of assembling path planning is improved. The multi-omics feedback correction module integrates transcriptome and epigenetic data, corrects an assembly result and inhibits error accumulation, and meanwhile, based on deep Q network model reinforcement learning and a dynamic resource scheduling module, assembly strategies and resource allocation are dynamically adjusted, efficient utilization of computing resources is achieved, and resource waste is reduced.

The invention belongs to the technical field of biological data processing, and discloses a multi-omics data fusion method and system based on a hypergraph network. According to the method, a hypergraph structure is adopted for modeling omics data, high-order interaction information can be more efficiently mined, and a complex association mode between biological entities can be more comprehensively revealed; by introducing a hypergraph aggregation mechanism, high-order complex relationships in various omics data can be represented more accurately, the limitation that a traditional method can only capture the relationship between every two nodes is overcome, and the understanding ability of the model to the interaction between biological entities is improved; according to the hypergraph fusion method, multi-omics specific hypergraphs are effectively integrated, unified representation of cross-omics data is realized, the representation capability of the model for complex multi-relational data is enhanced, and more comprehensive technical support is provided for multiple downstream tasks.

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 discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention relates to the technical field of protein related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, and performing polymorphic context decoding to obtain a protein expression context matrix; analyzing interaction of behavior characteristics in the protein expression context matrix to obtain a multi-scale causal structure map; executing structure-function transformation rule extraction in the multi-scale causal structure atlas to obtain a function mapping unit set; performing evaluation based on the function mapping unit set so as to form a configuration decision diagram; simulating paths in the configuration decision diagram, and performing path adaptability scoring to obtain a path adaptability feedback table; and optimizing the path adaptability feedback table to obtain an optimal expression configuration set. According to the method, the precision, the stability and the controllability of protein information in the process of structural analysis, function recognition and regulation path construction can be improved.

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 relates to a hepatocellular carcinoma gene knockout target library based on multiple omics and a screening method of the hepatocellular carcinoma gene knockout target library, and the gene knockout target library for precise treatment of hepatocellular carcinoma is finally obtained through data collection and integration, data screening and target verification in sequence. According to the invention, through multi-omics data integration and bioinformatics analysis, key driving genes of hepatocellular carcinoma are systematically screened, and the important effects of the genes in occurrence, development, metastasis, drug resistance and immune escape of hepatocellular carcinoma are disclosed; the genes not only deepen the understanding of the hepatocellular carcinoma molecular mechanism, but also provide important theoretical basis and potential intervention targets for the development of targeted therapy and personalized therapy strategies.

The invention discloses a differential gene regulation and control network reconstruction method based on mutual information and a redundancy regulation and control filtering system. The method comprises the following steps: firstly, collecting gene expression data under two or more conditions, respectively calculating mutual information of gene pairs under each condition, and screening the gene pairs with obvious mutual information difference as candidate regulation edges; furthermore, a redundancy regulation and control relation caused by the intermediary variables is identified and eliminated through calculation condition mutual information, and finally a difference regulation and control network with high credibility is constructed. The system comprises a data preprocessing module, a mutual information calculation module, a redundancy indirect regulation and control effect filtering module, a regulation and control direction judgment module, a network construction and threshold optimization module, a difference network construction module and an output and visualization module. The method can effectively improve the biological interpretability and inference accuracy of the network, and is widely applied to the bioinformatics fields such as disease mechanism research, regulatory factor identification and multi-omics integrated analysis.

The invention provides a multi-omics data missing interpolation method and system based on a multi-view auto-encoder, and relates to the technical field of biological multi-omics data. The method comprises the following steps: firstly, acquiring multi-omics data related to a disease, and preprocessing the multi-omics data; then special encoders are constructed for different omics data; introducing a self-attention mechanism and a cross-attention mechanism to enhance the feature expression ability and realize cross-omics feature fusion based on the potential feature expressions of each group of science extracted by each encoder to obtain the fusion feature expressions of each group of science; then, adopting a hierarchical fusion strategy to further integrate feature representations of each group of chemical fusion into unified potential feature representations to realize deep fusion and compression of information; finally, specialized decoders are designed for encoders of each group for original data reconstruction; according to the method, the problem of data missing generally existing in multi-omics integration is solved.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention discloses a method for screening a Wilson disease serum exosome miRNA biomarker based on a transcriptomics technology, and belongs to the field of bioinformatics analysis of diseases. According to the invention, the effect of miRNA in WD pathogenesis is discussed by identifying serum exosome miRNA, and a potential biomarker is provided for accurate diagnosis and treatment of the disease. According to the invention, 59 DE-miRNAs are identified by analyzing the expression of differential miRNAs of WD and control group patients, and the DE-miRNAs are related to key approaches such as metabolic regulation, cancer progression, signal transduction and the like. Besides, the reliability of the key DE-miRNA, including miR-451a, miR-204-5p and the like, is confirmed through experimental verification, and the potential of the key DE-miRNA as a non-invasive biomarker is indicated. Research results provide important theoretical basis and new insight for accurate diagnosis of WD and development of potential therapeutic targets.

The invention discloses a method for treating piglet damp-heat diarrhea through multi-omics combined analysis. According to the method, faeces metabonomics, intestinal transcriptomics and intestinal flora 16s analysis are adopted, transcriptome data, metabolome data and intestinal flora data are screened according to a prime difference standard, then transcriptomics of a model group and a control group, faeces metabonomics and intestinal flora are subjected to conjoint analysis, an intersection is taken, and the faeces metabonomics of the model group and the control group, the faeces metabonomics of the model group and the control group and the intestinal flora of the model group and the control group are subjected carrying out conjoint analysis on transcriptomics of the Model group and HQ-M, faeces metabonomics and intestinal flora, and then taking an intersection; the method comprises the following steps of: selecting a plurality of metabolites and florae from a plurality of intestinal florae, respectively obtaining intersections, screening related genes, searching gene FPKM expression quantity in transcriptomics, and screening corresponding metabolites and florae in metabonomics and intestinal florae to obtain a gene-flora-metabolite related network diagram. Multi-omics combined analysis shows that the piglet diarrhea due to damp-heat is mainly manifested by inflammation and glucose and lipid metabolism disorder.

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.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention discloses a cancer prognosis prediction method and system based on multi-omics fusion. The method comprises the following steps: acquiring multiple groups of omics data; preprocessing the plurality of groups of omics data to obtain a plurality of groups of first omics data features; inputting the plurality of groups of first omics data features into a multi-head attention-based graph convolutional network feature extraction model to obtain a plurality of groups of second omics features; inputting the plurality of groups of second group of characteristics into an attention mechanism fusion module based on biological priori knowledge guidance to obtain fusion characteristics; and inputting the fusion features into a prognostic scoring model to obtain prognostic scores. In addition, potential gene targets related to cancer prognosis are obtained by adopting an omics data feature importance evaluation method. The prognosis prediction conclusion obtained by the method is high in accuracy and high in interpretability.

The invention relates to the technical field of health risk prediction, in particular to a metabonomics-radiomics prediction method for chronic subdural hematoma recurrence risk, which comprises the following steps: acquiring a CT image and extracting edge gray fluctuation, constructing fluctuation parameters in combination with metabolome data, screening coordination characteristics to generate a risk combination, and predicting a chronic subdural hematoma recurrence risk. Feature pairs consistent in trend are extracted to form a collaborative channel, and a feature matrix is constructed to generate an input vector set; according to the method, disturbance features are extracted through a CT image edge gray level path, a cross-modal fluctuation trend comparison mechanism is established in combination with patient brain metabolism indexes, biological consistency between the features is enhanced, feature combinations with uncoordinated changes are eliminated, feature pairs with collaborative structure and function trends are screened, and a linkage path is constructed. The evolution relation from structural disturbance to metabolic response is reflected, channel data sorting and recombination improve the difference of input characteristics, the stability and accuracy of recurrence discrimination are enhanced, and the systematicness and interpretability of risk assessment are improved.