682 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.

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

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 provides a corn genotype and environment cross-modal feature fusion genome prediction method and model, a deep learning method is adopted to model a complex nonlinear transformation relationship between different omics data including a genome and an environment group, and a GECFGP model is constructed to fuse corn cross-genotype and environment interaction features. Time sequence characteristics and important growth periods related to phenotypes are well mined, and the function of predicting the phenotypes of the complex agronomic traits of the corn is achieved. The method shows high robustness in different experimental settings, has superiority in genome prediction of G * E interaction, explores phenotype-related important growth periods and performance of predicting phenotypes across different years, provides a new thought for a genome prediction analysis method, and has wide application prospects. Therefore, theoretical and application value reference is provided for adaptability analysis of corn in different environments and exploration of key growth periods, and development of the field of intelligent breeding is accelerated.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention relates to a method for dividing cell types based on single-cell multi-omics data, and belongs to the technical field of bioinformatics. The problems of high noise and high sparsity formed by a common technology are solved. Comprising the following steps: step 1, constructing a deep neural network model, step 2, obtaining a single-cell multi-omics comprehensive data set, step 3, preprocessing the single-cell multi-omics comprehensive data set, step 4, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set, and step 5, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set. And performing cell type division on to-be-detected data based on the trained deep neural network model. According to the method, an scDRMAE model is provided, dependency relationships of different omics are captured through two parallel MAE modules, factors which are usually ignored are considered in cell clustering, interpolation of different omics data is realized by means of a mask prediction mechanism in the cell clustering, and finally, the omics data are effectively fused through an attention mechanism, so that the accuracy of cell clustering is improved. And the accuracy of model cell clustering is improved.

The invention relates to the technical field related to artificial intelligence, in particular to an intelligent medical diagnosis engine based on an AI large model, which comprises an auxiliary diagnosis module for collecting data such as an electronic medical record and a medical image of a patient and predicting an auxiliary result by using an integrated learning method; the drug recommendation module is used for analyzing drug recommendation data by using an AI large model and predicting potential curative effects and side effects of new drugs; and the operation simulation module is used for constructing an operation environment and a human body model in combination with a virtual reality technology. A large amount of medical data can be quickly processed and analyzed through auxiliary diagnosis, the diagnosis time is shortened, gene, physiological and pathological multi-omics data of a patient can be integrated by reducing unnecessary invasive examination and misdiagnosis and drug recommendation, a personalized drug treatment scheme is provided for the patient, and the patient experience is improved. Comprehensive and accurate diagnosis basis and treatment suggestions are provided for doctors; by accurately simulating a real operation scene including mechanical and optical characteristics, a more real training experience is provided for doctors and medical students.

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 multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

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 the technical field of biological information mining, in particular to a multi-omics data optimization method and system for intestinal flora matching and a medium. The method comprises the following steps: acquiring historical intestinal tract multi-omics data and integrating the historical intestinal tract multi-omics data to obtain host intestinal tract multi-omics data; performing multi-omics data fusion based on the host intestinal multi-omics data to obtain microbial flora fusion data; performing pattern classification according to the host intestinal tract multi-omics data to obtain host intestinal tract physiological pattern data; carrying out physiological pattern influence factor calculation on the microbial flora fusion data to obtain host intestinal tract physiological pattern influence factors; and performing host intestinal flora matching on the microbial flora fusion data and the host intestinal physiological data according to the host intestinal physiological pattern influence factor, thereby obtaining intestinal flora matching data. According to the method, the accuracy of flora matching prediction can be improved.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

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 a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention provides a method for identifying cell communication based on multi-omics data. The method is realized by the following steps: step 1, acquiring a single cell and space transcriptome data set; 2, preprocessing the single cell and the spatial transcriptomics data set; and step 3, constructing a deep neural network model. And step 4, training the constructed deep neural network model by using the preprocessed single cell and the space transcriptomics data set. And 5, carrying out intercellular communication identification on to-be-detected data by using the trained deep neural network model. According to the method, an excellent inter-cell communication recognition effect can be achieved on multiple sets of single-cell and spatial transcriptomics data sets. Compared with other latest intercellular communication identification methods, the method shows more excellent performance on a plurality of evaluation indexes. In addition, under the condition of different degrees of noise and on a missing space transcriptome data set, high stability and accuracy are achieved.

Disclosed are a disease analysis method, a training method and apparatus of a disease analysis model, the disease analysis method includes: acquiring first omics data and second omics data of a patient, wherein first omics includes a plurality of first loci and second omics includes a plurality of second loci; inputting the first omics data and the second omics data into a fusion algorithm model to obtain a predicted result of the patient, wherein the fusion algorithm model is formed by fusion and construction according to a first omics model and a second omics model, the first omics model is constructed according to sample data of the first omics, and the second omics model is constructed according to sample data of the second omics.

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 belongs to the technical field of health assessment, and particularly relates to an aging clock construction method based on a causal machine joint model. The method comprises the steps of 1, obtaining a multi-modal data set of a target crowd based on multi-modal data fusion and standardization preprocessing; 2, performing two-stage screening on the multi-modal data set to obtain core features; 3, analyzing interaction among multiple groups of schools based on the extended proportion advantage logistic regression hybrid model, and then carrying out P value calibration based on a fused saddle point approximation method; 4, constructing an aging clock based on a mixed causal effect graph model, evaluating a multi-dimensional causal relationship, and calculating the biological age of the individual; according to the method, clinical phenotype data and multi-omics data are fused, a machine learning and causal inference combined method is adopted, the individual aging degree and health risk are accurately analyzed, a high-performance aging clock model is provided for primary medical institutions, and scientific basis and decision support are provided for health management and early disease intervention.

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 relates to the technical field of classified storage of biological data, in particular to a classified storage method and system for biological cell data. The method comprises the following steps: acquiring multisource cytomics data; performing data standardization on the multisource cytomics data to obtain standard cell characteristic data; performing characteristic matrix conversion on the standard cell characteristic data to obtain a cell characteristic digital matrix; performing molecular fingerprint construction on the biological cells based on the cell characteristic digital matrix to obtain cell molecular characteristic fingerprint data; performing hierarchical classification on the standard cell characteristic data to obtain a cell phenotype classification system; performing hierarchical labeling on the standard cell characteristic data according to the cell phenotype classification system to obtain cell phenotype labeling data; and performing ontology mapping on the cell phenotype labeling data to obtain a cell phenotype relationship network. According to the method, refined classification, dynamic updating and efficient storage of the biological cell data are realized.

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.

The invention provides an individualized management system and method for colorectal cancer risk prediction, and relates to the technical field of medical diagnosis and individualized medical treatment. The system comprises a multi-omics data processing module used for collecting a blood sample of a subject, extracting circulating free DNA, and sequencing cfDNA by adopting a simplified methylation sequencing technology to obtain final original data of DNA methylation characteristics; the risk prediction module is used for carrying out bioinformatics analysis on the original data of the final DNA methylation features by adopting a machine learning algorithm, extracting a methylation feature set, a tail end motif feature set and a breakpoint motif feature set, and carrying out risk prediction on the basis of the extracted feature sets; and predicting the colorectal cancer disease risk of the subject by using a pre-trained prediction model to obtain a prediction result. The problem that the risk prediction accuracy of the colorectal cancer is low is solved.

The invention discloses a spatial multi-omics data integration method based on graph attention and a multivariate loss function. Comprising the following steps that spatial multi-omics data and spatial position coordinates corresponding to the spatial multi-omics data are obtained, a preset spatial multi-omics data model is input, and the model comprises a graph attention encoder and a decoder; the spatial multi-omics data model constructs a graph structure according to spatial multi-omics data and spatial position coordinates, a graph attention encoder performs cell spatial multi-omics data fusion based on the constructed graph structure, and a decoder performs data reconstruction based on fused data to complete cell spatial multi-omics data integration; and constructing a multivariate loss function to carry out iterative optimization on the cell space multi-omics data integration process of the space multi-omics data model, and outputting an optimal cell space multi-omics data integration result. According to the method, the batch effect is solved, the smoothness of a final result in space and the accuracy of spatial analysis are improved, and the integration of multi-omics data of the cell space is realized.