1460 results about "Bio informatics" patented technology

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a DNA binding residue prediction method based on a multi-modal protein language model in the technical field of bioinformatics. The DNA binding residue prediction method based on the multi-modal protein language model comprises the following steps: sequentially using tools to respectively obtain feature files of a protein sequence P to be subjected to DNA binding residue prediction; inputting all the feature files into the ESM3 to obtain an embedded matrix M; the method comprises the following steps: processing a protein sequence into a residue sample, constructing an isovariant graph neural network model according to a protein structure diagram, constructing a data set by utilizing the protein sequence of known DNA binding residues, and training the constructed network; and inputting a residue sample of the protein sequence into the trained model to obtain a DNA binding residue of the protein sequence. According to the DNA binding residue prediction method based on the multi-modal protein language model, the efficiency and accuracy of DNA binding residue prediction are improved.

The invention provides a drug-disease association prediction method and system based on heterogeneous node sequence representation, and belongs to the technical field of drug discovery in bioinformatics. Constructing a drug similarity network and a disease similarity network; constructing a heterogeneous biological interaction network by using the drug similarity network and the disease similarity network, and obtaining a first-order neighbor subgraph of the target node; splitting the first-order neighbor subgraph of the target node into a plurality of meta-relation bigraphs according to the meta-relation type, performing intra-domain message propagation and aggregation in each meta-relation bigraph, and updating the semantic features of the target node; fusing the semantic features of the target nodes in a cross-domain manner, updating the sequence features of the target nodes, and aggregating the features; obtaining vector representation of a target node through a multi-head self-attention mechanism by using the updated sequence features; and inputting the vector representations of the drug nodes and the disease nodes into a multi-layer perceptron to predict the drug-disease association probability. Semantic information confusion is effectively avoided, and then the accuracy of model prediction is improved.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention provides recombinant human III-type collagen which is of a triple-helix structure and assembled into collagenous fibers and application of the recombinant human III-type collagen, and belongs to the technical field of bioengineering. Collagen fragments capable of forming a triple helix structure are screened out through a bioinformatics algorithm, the fragments are connected in series, a key C-terminal structural domain and a specific amino acid sequence are combined, and formation of a stable triple helix advanced structure and assembly of collagenous fibers are effectively promoted. The recombinant human III type collagen is efficiently expressed through a eukaryotic expression system, and shows excellent stability, low immunogenicity and good biocompatibility. Experimental results show that the prepared recombinant human III-type collagen not only has a natural triple helix structure, but also can be further assembled into macromolecular collagen fibers, is suitable for multiple fields such as medical beauty, bioengineering and tissue repair, and has wide application prospects and market values.

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

The invention discloses a soil health and microbiological analysis intelligent environment monitoring device, and relates to the technical field of soil monitoring, the device comprises the following components: an outer shell, a sensor assembly, a microflora interaction monitoring sensor group, a data acquisition and transmission module, a power supply module, a microprocessor, a control unit and a data analysis module; signal molecule concentration change data and predatory relationship visual data are combined through the data analysis module, deep analysis is carried out by using bioinformatics and mathematical modeling means, and a multi-dimensional regulation network hidden in a microbial community can be comprehensively revealed; according to the soil health assessment device, the interaction relationship between the soil ecological system and the soil ecological system is analyzed accurately, the influence of the interaction relationship on key ecological functions such as material circulation and energy flow in the soil ecological system is analyzed accurately, the device can generate a detailed soil health assessment report based on the analysis results, agricultural scientific researchers, planting farmers and environmental protection monitoring departments can assess the soil health condition accurately, and the soil health assessment efficiency is improved. And targeted soil management measures are taken.

The invention discloses a nucleic acid binding protein recognition method based on a protein map and a protein language model, and belongs to the technical field of bioinformatics. According to the method, high-dimensional sequence features extracted by a ProtT5 protein language model are combined with three-dimensional structure information predicted by AlphaFold, so that the model can capture rich feature information of the sequence and the structure at the same time, and the accuracy and reliability of NABPs prediction are remarkably improved; the expression capability of sequence features is enhanced through the GAT network and the BiLSTM network, so that the constructed prediction model can more comprehensively understand important information in the protein sequence, and the prediction performance of NABPs is improved; the features output by the GAT network and the BiLSTM network are fused, and NABPs classification is performed through the MLP, so that the prediction model can make full use of feature information from different sources, and the NABPs prediction performance and robustness are improved.

The invention relates to an RNA-protein binding site discrimination method based on a convolutional block attention mechanism dense network, and belongs to the field of bioinformatics. Firstly, a public RNA-protein binding site data set is acquired, then, a feature space is constructed by adopting four feature extraction methods, and finally, a dense connection convolutional network improved based on a convolutional block attention mechanism is used for learning a long-term dependency relationship in a combination capture sequence of advanced local features, a bidirectional gating circulation network and a self-attention mechanism. The RNA-protein binding site is discriminatively analyzed by creatively using the dense network improved based on the convolutional block attention mechanism in combination with the idea of the bidirectional gating loop network and the self-attention mechanism, richer feature information can be learned and extracted, and then the model discrimination accuracy is improved.

The invention provides a protein expression prediction method based on a convolutional neural network and a residual attention mechanism, and belongs to the field of bioinformatics and artificial intelligence. The method comprises the following steps: performing data cleaning and binary one-hot coding on a DNA sequence, inputting the coded DNA sequence into a feature extraction layer, and extracting local features of DNA; local features are extracted through a convolutional neural network, global features are obtained through a multi-head attention mechanism, and residual connection is combined; and finally, inputting the global features into a full connection layer to generate a protein expression prediction result. Furthermore, an SHAP interpretability method is utilized to interpret the output result of the prediction model, and the contribution of the basic group at each position in the DNA sequence to the predicted value is quantified. According to the method, deep learning and interpretability technologies are combined, the precision and credibility of protein expression prediction are remarkably improved, and the method is suitable for predicting protein expression.

The invention relates to an antibacterial peptide generation method and device based on a generative model and computer equipment, and relates to the technical field of artificial intelligence and bioinformatics. Predicting the novel antibacterial peptide on the basis of the existing antibacterial peptide sequence by using a pre-trained generative model to obtain a predicted sequence; inputting the prediction sequence into a pre-trained antibacterial peptide classifier, and judging whether the prediction sequence has antibacterial activity or not; the antibacterial peptide classifier is obtained by training based on an antibacterial peptide tag set and a non-antibacterial peptide sequence tag set; and outputting the prediction sequence with the antibacterial activity as a final target antibacterial peptide sequence, so that the problem of low antibacterial peptide design generation efficiency is solved, de novo generation and judgment of the antibacterial peptide can be realized by virtue of a generative model and a classifier, and the generation efficiency and accuracy are improved.

The invention provides a drug-disease association prediction method and system based on cross-view comparative learning, and belongs to the technical field of drug discovery in bioinformatics. Constructing a semantic view and an interactive view; constructing and training a drug-disease association probability prediction model: integrating the semantic view and the interaction view to obtain fused drug vector representation and disease vector representation as a fusion view; using the fused drug and disease vector representation to output a drug-disease association probability; performing cross-view comparison learning on the fusion view and the semantic view; performing cross-view comparison learning on the fusion view and the interactive view; implicit knowledge is migrated from comparative learning to drug-disease association probability prediction through bottom-layer shared parameters in a multi-task learning mode. And the problem that necessary association information between the medicine and the disease is discarded due to comparison view enhancement by adopting random disturbance is avoided, so that the accuracy of medicine-disease association prediction is improved.

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 discloses a primer combination for detecting monkey pox viruses and identifying monkey pox viruses Ia, Ib and II, and belongs to the technical field of molecular biology, the nucleotide sequences of specific primers and probes are shown as SEQ ID NO.1-SEQ ID NO.12, and the primer combination aims at solving the problem that in the prior art, new evolutionary branch Ib types of monkey pox viruses cannot be accurately typed, and the detection sensitivity is high. According to the present invention, the defects that the monkey pox virus and the orthopox virus have the cross reaction are overcome, the genome difference of different evolutionary branches is analyzed through the bioinformatics system, the primer probe composition capable of simultaneously detecting the monkey pox virus and carrying out accurate typing is analyzed and screened, and the primer combination can detect 200 copies / mL of the monkey pox virus with high specificity and high sensitivity. Meanwhile, detection of the monkey pox virus and identification of the types Ia, Ib and II of the monkey pox virus are realized in a reaction system, so that different monkey pox branches can be distinguished, and treatment and prevention can be performed in a more targeted manner.

The invention discloses a genomics cross-modal condition generation method and system based on a potential diffusion model, and belongs to the field of bioinformatics, and the method comprises the following steps: respectively designing and constructing an auto-encoder for gene expression and gene mutation data, and carrying out model training; constructing a potential feature space according to an encoder of gene mutation data and training an unconditionally guided potential diffusion model; constructing a cross-modal feature alignment network according to the two trained encoders and training the cross-modal feature alignment network; and performing joint fine tuning on the unconditionally guided potential diffusion model and the cross-modal feature alignment network. According to the method, the problem of sample modal deletion in multi-modal learning is solved, potential correlation between gene expression data and gene mutation data can be captured through cross-modal feature alignment, information flow between different modals is enhanced, and high-quality prediction data is effectively generated when mutation data is deleted through the potential diffusion model.

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 bioinformatics, in particular to a method for identifying an elncRNAs regulation target gene in a single cell based on single cell ATAC-seq. According to the method provided by the invention, a regulatory network of an elncRNAs-target gene on a single cell whole genome is identified by utilizing chromatin co-accessibility based on scATAC-seq data. Meanwhile, the method is applied to immune cell differentiation, and the elncRNAs-target gene regulation dynamic state of immune cell differentiation is accurately recognized. In addition, the biological functions of the elncRNAs in different cell types are further analyzed. The method provided by the invention can help related researchers in the field of biomedicine to more elaborately and accurately predict the regulation effect and function of the elncRNAs under the single cell resolution.

The invention relates to the field of bioinformatics and machine learning, in particular to a rice genotype and phenotype association prediction method based on machine learning. The method comprises the following steps: collecting genotype data and phenotype data of a rice sample, and generating genotype features by adopting a dynamic feature extraction algorithm so as to obtain a genotype feature set; and S2, constructing a genotype and phenotype association prediction model, and predicting phenotypes based on the genotype feature set. The problems that feature information of genotype data is difficult to accurately capture due to non-uniform data distribution or periodic change of different gene loci, and complex features are difficult to recognize by an existing feature extraction method are solved; in the aspect of agricultural genotype and phenotype prediction, especially in the fields of rice breeding, disease resistance enhancement and yield optimization, an effective method special for bioinformatics feature extraction and correlation prediction is lacked; the technical problem of excessive dependence on some unimportant or redundant features due to insufficient performance during feature redundancy processing is solved.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer research.

The invention relates to the technical field of bioinformatics, in particular to a gene pool data retrieval method and system and a storage medium. The method comprises the following steps: acquiring multi-source genome data, performing quality control on the multi-source genome data based on an FAIR principle, and performing efficient storage and indexing by utilizing a distributed storage system so as to obtain a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, so as to obtain evolutionary relationship map data; mapping gene structures and functions among different species according to the evolutionary relationship map data so as to obtain cross-species gene mapping data; receiving retrieval query data input by a user; and performing query type identification on the retrieval query data to obtain optimized query data. Through the multilevel evolutionary tree model and cross-species gene mapping, gene function comparison and prediction among different species are realized.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on local graph features and global relationships, which comprises the technologies of deep learning, comparative learning, graph neural network and the like. Firstly, data processing is carried out, input data is prepared, and initial representation of a molecular map is constructed. Secondly, extracting local features of the molecular map through a local topological structure feature network, and focusing on local information of atoms in molecules and adjacency relations of the atoms; secondly, performing global feature extraction on the molecular graph by utilizing a global attention network, and capturing a global dependency relationship and long-range interaction among molecules; and finally, combining the local features and the global features through feature fusion, and performing final prediction. According to the method, through a double-branch network architecture, details of local structures in molecules can be concerned, and global interrelationships among the molecules can be captured, so that more comprehensive and accurate feature representation is provided for prediction of molecular attributes.

The invention relates to the technical field of gene analysis bioinformatics, and particularly discloses a gene data analysis method based on a large language model.The method comprises the steps that the quality of gene data to be analyzed is analyzed, whether the gene data to be analyzed is available or not is judged, and a gene sequence segmentation scheme of the available gene data is determined; inputting the gene data after gene sequence segmentation into the large language model to obtain a gene data association feature data set and a gene data analysis result, determining a gene data analysis accuracy feature value, and judging whether the gene data analysis result conforms to expectation or not; the problems that quality and integrity problems of traditional biological data challenge the accuracy of analysis results and effective integration and analysis of multi-modal data still need to be improved are solved, the data processing efficiency is optimized in the whole process, repeated calculation, misjudgment and invalid results are reduced, the analysis efficiency is improved, and resource waste is reduced.

The invention relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The invention relates to the technical field of bioinformatics, and discloses an adverse drug reaction prediction method based on comment text information enhancement, and the method comprises the following steps: constructing an adverse drug reaction dictionary; extracting adverse drug reaction information based on the comment text; integration of adverse drug reaction information; integrating the drug characteristic information of the public database; calculating the similarity; constructing a heterogeneous graph; building a graph convolutional network model; model training and evaluation; predicting adverse reaction cases of drugs; and analyzing the occurrence mechanism of adverse drug reaction. According to the adverse drug reaction prediction method based on comment text information enhancement, diversified data such as drug comments, clinical test data, actual medication experience and feedback are comprehensively utilized, patient features are added to enrich the relationship between drugs and adverse reactions, more comprehensive drug safety assessment is provided, and the prediction accuracy is improved. In addition, compared with other models, the GCN model has the advantages that the training and prediction are quicker, and the calculation difficulty can be reduced.

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.