506 results about "Omics data" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

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 rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

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 discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

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 relates to a nitric oxide data analysis method and system for bronchial asthma assessment. According to the method, exhaled air nitric oxide and pulmonary alveolar nitric oxide of a patient, omics data and clinical information are acquired in a standardized manner, and multi-dimensional features are constructed after data cleaning and fusion; comprise an inflammation level index, an inflammation region entropy, an inflammation synergy index, a glucocorticoid response factor, an infection-smoking synergy influence factor and a heredity-symptom distribution index. And outputting initial risk assessment based on an explainable elevator EBM model, and finally generating an asthma risk probability through a weighted integration formula to realize three-level layering: low / medium / high risk. The system dynamically associates treatment decisions, for example, the drug dosage and the monitoring frequency are improved when the risk is upgraded, and the medication scheme is optimized when the risk is degraded. According to the scheme, the limitation of traditional single marker static analysis is broken through, multiple mechanisms of dissection, immunity and pharmacology are fused, the evaluation precision and clinical applicability are remarkably improved, the acute attack rate is reduced, and accurate typing treatment is guided.

The invention provides a data registration method and device for a space transcriptome and a space metabolome, electronic equipment and a storage medium. The method comprises the steps of obtaining a space transcriptome image and a space metabolome image to be registered; performing image registration on the space transcriptome image and the space metabolome image through an image registration model to obtain an optimal registration parameter; and performing space coordinate registration on the data points of the space transcriptome image and the space metabolome image after image registration by using the optimal registration parameter. According to the method, the tedious process of manual registration can be eliminated, and the unification of the space coordinates of the two groups of data points can be quickly and accurately completed. In addition, the data registration method provided by the invention is low in cost and extremely short in time consumption, does not depend on any closed source business software, reduces the use cost, provides space and freedom for subsequent algorithm upgrading and iteration, and can flexibly adapt to continuously changing research requirements.

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 discloses a dynamic identification method for unusual cells before young tumors based on multi-omics data, and relates to the technical field of cell unusual identification. A dynamic correlation intensity matrix and a cumulative effect contribution matrix are constructed, a differentiation screening strategy is implemented according to individual response characteristics, and the unusual cells before young tumors are identified. And the abnormal dynamic high-fidelity identification of the young tumor pre-cells is realized. And aiming at individuals of different response types, an instant path, a long-term path or a double-path fusion strategy is respectively adopted, key behavior data is accurately screened, and the input quality is improved. According to the method, redundant interference is effectively eliminated, the simulation capability of the model on key processes such as immunosuppression and DNA damage accumulation is enhanced, the biological rationality and prediction precision of a cell state evolution sequence are remarkably improved, and the problems of model response lag, low calculation efficiency and output distortion caused by data noise in the prior art are solved; and a reliable technical support is provided for early warning and individualized intervention of precancerous lesions.

The invention discloses a non-paired single cell multi-omics data gene regulatory network inference method, which comprises the following steps: collecting single cell sequencing non-paired data, and preprocessing the data; inputting the pre-processed single cell sequencing non-paired data into a multi-layer variational auto-encoder structure to generate advanced potential variables and reconstructed single cell sequencing non-paired data; training a layered GAN by adopting a layered adversarial alignment mechanism according to the advanced potential variables and the reconstructed single cell sequencing non-paired data; according to the advanced potential variables, adopting a mutual nearest neighbor strategy to train a mutual nearest neighbor module; fusing the advanced potential variables of different modes by adopting a gating fusion mechanism, constructing a unified advanced potential variable, and completing adaptive feature integration of the multi-mode advanced potential variables; according to prior information, an initial adjacency matrix is established, a gene regulation network is constructed in combination with unified advanced potential variables, and the gene regulation network with biological rationality is directly deduced from non-pairing input.

The invention discloses a deep learning driven spatial multi-omics data analysis and integration method, and relates to the technical field of cell sequencing, the method comprises the following steps: using each sequencing point location of different tissue slices as a node, using the adjacency relation of each sequencing point location as an edge, and constructing a spatial omics data graph structure; extracting dimension-reduced features from the space omics data graph structure, and embedding the dimension-reduced features as low-dimensional features; adjusting the coordinate information of the low-dimensional features of the different tissue slices through affine transformation so as to perform direction alignment on the sequencing point locations of the different tissue slices; according to the spatial information and the low-dimensional features, matching the sequencing point locations after the directions of the different tissue slices are aligned; and fusing the matched low-dimensional features according to the spatial information to obtain spatial multi-omics data after integration of different tissue slices. According to the method, an efficient analysis, alignment, matching and integration scheme is provided to assist in space omics data analysis and conjoint analysis, so that the accuracy and efficiency of data processing are improved.

The invention relates to the technical field of teasel breeding, and discloses a teasel germplasm resource multi-character collaborative screening breeding method, which comprises the following steps: multi-omics data acquisition: extracting teasel germplasm leaf DNA (deoxyribonucleic acid), and capturing exon regions of disease resistance and metabolism related genes by adopting a targeted sequencing technology to obtain an SNP (single nucleotide polymorphism) marker matrix; the method comprises the following steps: collecting root extracts, detecting medicinal components through ultra-high performance liquid chromatography-mass spectrometry, and constructing a metabolite abundance matrix; measuring phenotype data of plant height, root length, root weight, root rot morbidity and survival rate under drought stress; key site and metabolite correlation analysis: constructing a Bayesian network containing SNP markers, metabolite and phenotypes by using a graph database, and carrying out cross validation through a leave-one-out method. The dipsacus asper germplasm resource multi-character collaborative screening breeding method aims at solving the problem that a traditional breeding method lacks systematic integration analysis of genomes and metabolic pathways in dipsacus asper multi-character collaborative improvement.

Processes to spatially align single cells to yield a specimen map with single cell resolution are provided. Methods can perform spatial omics on a specimen to yield spatial omics data. The spatial omics data can be used in combination with single cell omics data to assign single cells to spatial coordinates to yield a resolved specimen map.

The invention discloses a cerebral apoplexy high risk group positioning evaluation system based on multi-modal data, and relates to the technical field of medical information science, the cerebral apoplexy high risk group positioning evaluation system comprises a cerebral apoplexy prevention and control management platform, and the cerebral apoplexy prevention and control management platform is in communication connection with the following modules: a multi-modal data acquisition and integration module, the multi-modal data collection module is used for collecting multi-modal data related to cerebral apoplexy from multiple channels and carrying out preprocessing operation on the collected multi-modal data. By integrating clinical data, image data, omics data and terminal health data, multi-dimensional information related to the cerebral apoplexy can be comprehensively captured, particularly, cerebral vessel digital twin is utilized to simulate hemodynamic characteristics, a plurality of data sources are fused in combination with a graph neural network, high-risk groups can be recognized more accurately, and the accuracy of cerebral apoplexy recognition is improved. The accuracy and reliability of risk prediction are remarkably improved, the problem of missing detection caused by dependence on a single data source in a traditional method is solved, and more powerful support is provided for early intervention.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line real-time monitoring method and platform. 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 spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

The invention relates to the technical field of bioinformatics and clinical medicine, and discloses a multi-omics data association analysis-based tumor early screening and typing early warning system, which comprises a data acquisition and preprocessing module for integrating standardized longitudinal multi-omics data; the dynamics and topology analysis module is used for generating topology fingerprints representing dynamic behaviors of the system through state space reconstruction and persistent coherence analysis; the causal inference and risk assessment module is used for calculating critical moderation indexes in parallel to synthesize risk indexes and constructing a dynamic causal network; and a collaborative diagnosis and report generation module. According to the system, risk indexes derived by critical moderation, topological fingerprints and a dynamic causal network are creatively combined, multi-modal information fusion is carried out through a collaborative diagnosis unit, and finally a comprehensive early warning report is generated. According to the invention, the accuracy and reliability of early risk early warning of tumors can be obviously improved, and a mechanism-level traceability basis is provided for clinical intervention.

The invention relates to a sperm cell analysis and diagnosis system based on a multi-modal large language model, which comprises a multi-modal data co-processing unit, a cross-modal semantic alignment module, a dynamic diagnosis decision engine and a self-adaptive evolution system, the four-dimensional data processing module is used for synchronously processing microscopic images, motion trail videos, biochemical detection data and four-dimensional input data of medical record texts and comprises a feature selector based on a gating attention mechanism. According to the sperm cell analysis and diagnosis system based on the multi-modal large language model, quantitative analysis of sperm movement chaos features is realized for the first time, a nonlinear dynamic evaluation standard is established, a cross-modal knowledge distillation and meta-learning migration framework is developed, a data annotation dependence bottleneck is broken through, and an interpretable clinical decision support system is constructed; dynamic updating and probabilistic suggestion of diagnosis rules are achieved, semantic analysis of single-cell multi-omics data is achieved, and molecular mechanism research results are converted into clinically available knowledge.

The invention relates to the technical field of medical care informatics and precision medical treatment, and discloses a hemodialysis patient precision medication system based on multiple omics and big data, and the system comprises a multi-source data collection module which collects the multi-source heterogeneous data of a patient and carries out the standardization and integration processing; the drug metabolism analysis module is used for analyzing drug metabolism paths and individual differences through multi-omics data, constructing a drug metabolism polymorphic model and outputting gene metabolism capability grades and biomarker risk tags; the initial medication decision-making module is used for constructing an initial medication decision-making model based on a federal optimization framework of a momentum enhancement and robust aggregation mechanism, and outputting a personalized initial medication scheme for erythropoietin and a vein iron agent; and the drug dosage adjustment module is used for dynamically optimizing the time node and dosage adjustment opportunity of drug administration through an asynchronous federal reinforcement learning framework in combination with a prospective parameter correction strategy. According to the invention, the intelligent medication system with strong robustness and fine decision is constructed.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

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 discloses a dual-channel fusion cancer drug response prediction method, and belongs to the technical field of biological information. The method aims at solving the problems that an existing prediction method is only limited to intra-modal feature extraction, and a complex nonlinear cooperative relation between a drug and cancer cells is difficult to capture. Multi-omics data, drug molecular structure information and cancer cell line drug reaction data are integrated from databases such as CCLE, GDSC and PubChem, and unified input features are formed through standardization and feature construction. Introducing a hierarchical double-attention conversion network into the first channel to carry out characterization learning on the multi-modal features of the drug and the cell line, and constructing a high-order attention neighbor interaction graph convolutional network in the second channel to capture graph structure information of cancer drug response. And carrying out adaptive weighting on output results of the two channels by using a PPO-based fusion module so as to realize a dynamic optimal decision. And finally, generating a drug sensitivity prediction result of the cancer cell line through a classification predictor.

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 relates to the technical field of intelligent medical diagnosis, in particular to a clinical microbial infection intelligent diagnosis system and method based on multi-omics data fusion, and the method comprises the steps: collecting and processing time sequence multi-omics data, and generating a transient infection state snapshot vector; constructing a time sequence state transition model, inputting the instantaneous infection state snapshot vector into the time sequence state transition model, and predicting and generating a future state vector; respectively calculating a pathogen threat index and a host imbalance index based on the future state vector; generating a comprehensive risk score based on the pathogen threat index and the host imbalance index, and determining an early warning grade according to a preset early warning grade threshold; generating a hierarchical intervention strategy based on the early warning level in combination with the pathogen threat index and the host disorder index; outputting a grading intervention strategy as a specific diagnosis and treatment suggestion; according to the invention, prospective early warning is realized, and the situation that the optimal treatment opportunity is delayed due to the change of waiting indexes is effectively avoided.

The invention discloses a diabetes cognitive impairment method based on metabonomics analysis and prediction, and relates to the technical field of biological information, and the method comprises the following steps: S1, obtaining metabonomics data and immunomics data from a peripheral blood sample of a diabetic patient, extracting relevant time sequence data aiming at glucose metabolism, and calculating the glucose metabolism related time sequence data; processing the sequence data by adopting a time sequence analysis algorithm to obtain time sequence change characteristics; s2, constructing a cross-omics interaction network according to time sequence change characteristics, integrating an incidence relation between metabolite concentration and immune factor expression, and setting a dynamic interaction mode; according to the diabetes cognitive impairment method based on metabonomics analysis and prediction, through multi-omics data integration and dynamic network analysis, the precision and reliability of diabetes cognitive impairment mechanism analysis are remarkably improved, and a theoretical basis is provided for precise intervention.

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.