179 results about "Disease Association" patented technology

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 discloses a multi-dimensional data disease association prediction method and device based on a heterogeneous graph neural network, and the method comprises the steps: collecting and integrating multi-source data, and obtaining an association relationship and an association weight between a disease and each factor; constructing a heterogeneous graph based on the incidence relation and the incidence weight between the disease and each factor, dynamically distributing the relation weight through an attention mechanism, optimizing the heterogeneous graph, and obtaining an optimized heterogeneous graph neural network model; and predicting a potential disease based on the optimized heterogeneous graph neural network model to obtain a prediction result, and integrating the prediction result into the heterogeneous graph neural network model to form a final disease network. According to the method, complex association between diseases can be comprehensively captured, the expression ability and prediction precision of the network are improved, multi-dimensional association is automatically learned through the heterogeneous graph neural network, the tedious process of manually setting meta-paths is avoided, the risk of human deviation is reduced, and meanwhile the analysis efficiency is 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 provides a miRNA-disease association prediction method based on a graph neural network, and relates to the technical field of graph neural networks. The method comprises the following steps: acquiring miRNA sequence, a disease description text, miRNA family information, a disease father-son relationship and miRNA-disease association relationship data, and constructing a heterograph after performing data alignment; based on the miRNA sequence and the disease description text, using a pre-training language model to obtain miRNA representation and disease representation as node features, and updating the heterogeneous graph; and constructing a heterograph MDA association prediction model, and inputting the updated heterograph and the to-be-predicted miRNA-disease pair to obtain an association prediction score of the to-be-predicted miRNA-disease pair. According to the method, the biological entity can be fully expressed in entity expression; according to the method, information of different types of nodes can be effectively aggregated on model construction, multi-depth message passing is executed on different types of edges, integration of heterogeneous information of the nodes and the edges is achieved, and improvement of miRNA-disease association prediction performance is facilitated.

The invention discloses a drug relocation method and system based on subgraph perception and a mixed graph neural network, and the method comprises the steps: generating a corresponding dynamic drug-disease associated subgraph # imgabs1 # for a target drug-disease pair in a drug-disease associated network # imgabs0 #; aiming at the medicine nodes and the disease nodes in the # imgabs2, respectively constructing a medicine sub-graph # imgabs3 # and a disease sub-graph # imgabs4 #; performing deep feature extraction on # imgabs5 by applying a multi-head dynamic graph attention neural network model, and performing feature extraction on # imgabs6 and # imgabs7 on the basis of fusion of a GCN model and a GAT model to obtain final feature representations of # imgabs8, # imgabs9 and # imgabs10; and fusing the three, inputting the fused three into a drug-disease association prediction model based on a multi-layer perceptron (MLP) network, and predicting the association relationship of the target drug-disease pair. According to the invention, efficient and accurate drug-disease association prediction can be realized.

Embodiments are directed to a method for determining relationships between genes, phenotypes, and diseases that includes extracting a first set of biological data containing gene data, phenotype data, and disease data from a database, extracting a second set of biological data from a database, creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents, training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes, creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes, and displaying at least one of the association scores to a user.

The invention provides a topology and biological feature fusion-based virulence gene prediction method, which comprises the following steps of: obtaining a to-be-detected gene; inputting the to-be-detected gene into a trained DAVGAE model, and predicting the correlation degree of the to-be-detected gene and the disease to obtain a disease gene correlation prediction conclusion; wherein the DAVGAE model comprises a data enhancement module, an encoder and an inner product decoder. The problems of data sparsity and heterogeneous data integration in gene-disease association prediction can be effectively solved at least through a DAVGAE model formed by a data enhancement module, an encoder and an inner product decoder.

An miRNA-disease association prediction method and apparatus, an electronic device, and a storage medium. The association prediction method comprises: acquiring initial association data of miRNAs and diseases; calculating miRNA similarity and disease similarity on the basis of the initial association data; on the basis of the miRNA similarity and the disease similarity, constructing a dynamic hypergraph, wherein the dynamic hypergraph comprises first node features of the miRNAs, first node features of the diseases, hyperedges connected to the first node features of the miRNAs, and hyperedges connected to the first node features of the diseases; obtaining second node features of the miRNAs and second node features of the diseases on the basis of the dynamic hypergraph; and obtaining an association prediction result of the miRNAs and the diseases on the basis of the second node features of the miRNAs and the second node features of the diseases. According to the prediction method, a hypergraph structure can be dynamically learned and updated, thereby better predicting the association between miRNAs and diseases, and improving prediction performance.

The invention relates to a drug-disease association prediction method and model based on NMF and GT. A drug-disease association prediction method based on NMF and GT comprises the steps of (1) obtaining a comprehensive similarity matrix of drugs and diseases according to drug fingerprint similarity, disease phenotype similarity and Gaussian kernel similarity of the drugs and the diseases; (2) extracting features of the comprehensive similarity matrix of the medicines and the diseases through non-negative matrix factorization to obtain characteristics of the medicines and the diseases; capturing a high-order structure relationship through a graph Transform to obtain similarity features of drugs and diseases; (3) respectively carrying out interactive fusion on the drug characteristics and the drug similarity characteristics and the disease characteristics and the disease similarity characteristics to obtain final drug and disease characteristic representations; and (4) carrying out association prediction according to the final characteristic representation of the medicine and the disease. The NMF and GT-based drug-disease association prediction method and model provided by the invention have good potential in the aspect of accurately predicting potential drugs.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The present invention provides a hypergraph-based drug-disease association prediction method, device and electronic device, which relates to the field of bioinformatics, including: obtaining a drug hypergraph and a disease hypergraph; based on a hierarchical contrastive learning algorithm, inputting the drug hypergraph and the disease hypergraph into a target hypergraph convolution model to obtain an initial drug feature matrix and an initial disease feature matrix; performing interactive information aggregation through the initial drug feature matrix and the initial disease feature matrix to obtain a target drug feature matrix and a target disease feature matrix; performing association prediction through the target drug feature matrix and the target disease feature matrix to obtain a drug-disease potential association matrix. The present invention realizes accurate prediction of drug-disease potential association.

The invention discloses a risk assessment stabilization method for senile chronic disease detection, and relates to the technical field of medical data analysis, and the method comprises the steps: carrying out federal dynamic time warping processing on encrypted patient data streams of a plurality of medical institutions, generating a joint feature space mapping matrix, and obtaining a standard feature tensor through homomorphic encryption; inputting the standard feature tensor into a dynamic medical knowledge graph construction module, and generating a knowledge graph embedding matrix through a space-time sensitivity enhanced cross-modal attention mechanism; and fusing the standard feature tensor and the knowledge graph embedding matrix, constructing a dynamic hypergraph structure, executing dual-channel hypergraph convolution calculation, and outputting a patient-knowledge joint embedding matrix. According to the method, in the construction of the dynamic knowledge graph, the time sensitivity weight of the entity relationship is quantified through the exponential decay function, and the time-space enhanced embedded matrix is generated in combination with the cross-modal attention mechanism, so that the time sequence discrimination of the concurrent disease association strength is improved.

The invention provides a microbe-disease association prediction method based on a meta-path. The method comprises the following steps: S1, calculating similarities of a disease, a microbe and a drug respectively; s2, constructing a heterogeneous graph containing microorganism nodes, disease nodes and drug nodes based on the isomorphic networks of microorganisms, diseases and drugs; s3, feature representation of the microbial nodes and the disease nodes in the same meta-path is aggregated through attention in the meta-path; s4, obtaining final representations of the microbial nodes and the disease nodes; and S5, predicting the association probability of the microorganism and disease pair, and distinguishing whether there is an association relationship or no association relationship. According to the method, not only are feature representations from different similarity networks fused by adopting the MOSFEL, but also structure and semantic information of nodes are introduced through a meta path, and the information is adaptively aggregated through local attention and global attention, so that the method has certain value for actual disease diagnosis and treatment and subsequent drug research and development.

The invention discloses a construction method, a prediction system and a prediction method of a circular RNA (Ribonucleic Acid) and disease association prediction model based on a shared unit, and association prediction is realized through the constructed circular RNA and disease association prediction model. Comprising the following steps: constructing a circular RNA meta-path network, a circular RNA similarity network, a disease meta-path network and a disease similarity network based on a known associated data set, and respectively carrying out feature extraction on the networks to obtain circular RNA similarity features, circular RNA meta-path features, disease similarity features and disease meta-path features; the similarity features and the meta-path features are simultaneously input to a sharing unit; similarity features output by the sharing unit are input into a multi-layer perceptron to carry out circular RNA and disease association prediction and update model parameters. A sharing unit is designed for the model, and the prediction performance of the model is improved by constructing a similarity network and a meta-path network of circular RNA and diseases and capturing potential cross-view information in a multi-view feature fusion process.

The application discloses a kind of based on shared unit's circular RNA and the construction method of disease association prediction model, prediction system and prediction method, and the association prediction is realized by the circular RNA and the disease association prediction model constructed, comprising: based on known association dataset constructs circular RNA meta-path network, circular RNA similarity network, disease meta-path network and disease similarity network, the feature extraction of the aforementioned network respectively obtains circular RNA similarity feature, circular RNA meta-path feature, disease similarity feature and disease meta-path feature;Similarity feature and meta-path feature are simultaneously input into shared unit;The similarity feature output by shared unit is input into multilayer perception machine to carry out the association prediction of circular RNA and disease and update model parameter.Share unit is designed for model, and the similarity network and meta-path network of circular RNA and disease are constructed, and potential cross-view information is captured in the process of multi-view feature fusion, so as to improve the prediction performance of model.

The invention discloses a drug relocation method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical domain knowledge heterogeneous graph; generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair based on the biomedical domain knowledge heterogeneous graph; generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network; and based on disease knowledge embedding, drug knowledge embedding, disease structure embedding and drug structure embedding, obtaining a drug relocation result. According to the method, complex biological network characteristics are accurately captured and complex entity information is finely modeled through an innovative drug relocation model, so that accurate drug relocation is realized.

The invention provides an intelligent health management method and system for chronic disease patients. The system comprises a physical sign abnormity module, a common disease association module, a common disease repulsion association module and a strategy module. The physical sign abnormity module can collect physical sign data, judge abnormal risk conditions of chronic diseases, and obtain abnormal physical sign data of the chronic diseases when judging that the chronic diseases have abnormal risks; the co-disease association module comprises associated diseases associated with chronic diseases, and the association degree of the chronic diseases with abnormal risks and the associated diseases is obtained; the co-disease exclusion association module comprises exclusion diseases with treatment counteracting effects with chronic diseases, and the chronic diseases with abnormal risks and the exclusion association degree of the exclusion diseases are obtained; and the strategy module allocates a corresponding common disease treatment strategy according to the association degree between the chronic disease with the abnormal risk and the associated disease and the exclusion association degree between the chronic disease and the exclusion associated disease. According to the invention, the system optimization is improved.

The invention discloses a multi-view graph converter incremental learning method for identifying related diseases. The method comprises the steps of 1, heterogeneous network construction, 2, mask contrast learning, 3, graph converter feature extraction, 4, channel attention fusion, 5, depth matrix decomposition prediction and 6, incremental learning updating. According to the method, a heterogeneous network is constructed to integrate multi-source similarity data, mask contrast learning is utilized to generate multi-view embedding so as to reduce noise, node position information is coded by means of a graph converter, multi-view features are fused through a channel attention mechanism, disease association is further predicted through depth matrix decomposition, and the disease association prediction accuracy is improved. And an incremental learning strategy is designed to realize dynamic updating of new data, a new result is obtained under the condition that original input data and model retraining are not needed, and the method can adapt to the condition of explosive increase of the data.

The invention belongs to the technical field of biological information and computers, and particularly relates to a medicine-disease association prediction method based on matrix completion and double-strategy graph reasoning. The method comprises the following steps: firstly, calculating the similarity of Gaussian kernels and Laplacian kernels of drugs and diseases, and then fusing the Gaussian kernels and the Laplacian kernels by using a nonlinear fusion technology. According to the method, an adjacent matrix of a drug-disease heterogeneous network is taken as a target matrix, and the robustness and formability of the edge of a drug-disease association network are enhanced through truncation arc tangent rank minimization. The method mainly focuses on adjacent nodes of drugs and diseases, and potential noise of farther nodes is filtered out. In addition, the method adopts top-down and bottom-up strategies to predict the drug-disease association based on the drug-disease heterogeneous network. The cooperative effect of the double strategies can enhance the comprehensive processing capability of a complex structure and cross-domain association in the heterogeneous graph, and sufficient utilization of rich information in the network is ensured.

The invention discloses a pi RNA and disease association prediction method, device and equipment based on comparative learning. The method comprises the following steps: step S1, constructing a p RNA-disease heterogeneous graph network; s2, fusing the multi-level semantic information; step S3, according to the node representation, introducing a Transform model, and enhancing the global association modeling capability of the piRNA and the disease node; s4, constructing a topological graph and a semantic graph; step S5, obtaining an accurate p iRNA-disease association prediction score; the prediction device comprises a heterogeneous graph construction unit, a node embedding learning unit and a node embedding learning unit. An input and output unit, a storage unit, a communication unit, an RAM unit, an ROM unit and a GPU of the electronic equipment are connected with one another through a bus, and the requirements for complex calculation and data interaction of a p-RNA and disease associated prediction task are met. The method has the characteristic of high prediction accuracy.

The invention relates to a double-gene rare variation and disease relevance prediction model and an establishment method and application thereof, and belongs to the technical field of biological medicines.The establishment method of the double-gene rare variation and disease relevance prediction model comprises the following steps that S1, a sample library is screened; s2, performing quality control on whole exome sequencing data (WES); s3, performing phenotype screening; s4, performing grouping design; s5, carrying out PheWAS logistic regression analysis; s6, performing Firth logistic regression analysis and verification; and S7, carrying out double-gene feature analysis and double-gene pathogenicity relevance prediction. The method for analyzing the correlation between the rare double-gene variation and all disease phenotypes is designed for the first time, and a new method is provided for screening hereditary pathogenic factors of various diseases.

The invention relates to the technical field of bioinformatics, and provides a CDA prediction model composed of an original channel and an interaction channel. The method comprises the following steps: firstly, acquiring original data from two open source databases of circ2Disease and circR2Disease; the method comprises the following steps: processing original data, screening out human circRNA and disease association, constructing a sparse matrix, carrying out multiple similarity calculations based on the sparse matrix to obtain initial features, and then entering two channels: extracting high-order features and retaining important information by using a double-attention mechanism in a channel I; in the second channel, degree segmentation and Wave modules are used, and potential information association of different sample features is fused. And then, the information of the two channels is integrated through a channel fusion module to generate final output, and finally, a model with optimal performance is found through five-fold cross validation and evaluation indexes. Experimental results based on two data sets show that the performance of the model in a CDA prediction task is superior to that of an existing method, and further case research and generalization analysis verify the effectiveness and wide applicability of the model.

The embodiment of the invention provides a disease dynamic prediction method and device based on multi-source factors, a medium and a program product, and relates to the field of intelligent medical treatment. Through adaptive segmentation normalization and multi-source feature fusion technologies, the heterogeneity problem of multi-source physiological data is effectively solved, and the robustness of feature expression is remarkably improved while key physiological events are reserved; according to the dynamic prediction model based on the space-time-disease association tensor and the disease collaborative gating, explicit modeling of the three-dimensional relationship of the disease category, the physiological index and the time step is realized for the first time, so that a specific physiological index time sequence mode dependent on diseases such as arrhythmia and the like is accurately captured; the accuracy of multi-label prediction is greatly improved through coding disease co-occurrence prior; in combination with a dynamic width full-connection layer and a gradient-driven adaptive optimization strategy, the model can automatically adjust a network structure and training parameters according to the complexity of input features, and the accuracy is improved.

The invention designs a miRNA-disease association prediction method based on an adaptive content guided fusion mechanism and multi-scale gated convolution. Firstly, miRNA function similarity, disease semantic similarity and Gaussian interaction spectrum kernel similarity of the miRNA function similarity and the disease semantic similarity are calculated, the weight of each similarity matrix is adjusted through a self-adaptive content guide fusion mechanism, and similarity information contributing to a prediction task is automatically concerned. The method comprises the following steps: firstly, carrying out feature extraction through multi-scale gating convolution by combining Laplacian features and Weisfeier-Lehman absolute role coding features, and finally, connecting learned feature representations of miRNA and diseases, and sending the feature representations into an XGBoost classifier for training and predicting potential association of miRNA and diseases. The working mechanism of miRNA in diseases is deeply explored, practical application value is achieved for clinical diagnosis and prevention, and meanwhile discovery of a novel therapy is promoted.

The invention relates to the technical field of drugs, in particular to a drug relocation method for integrating multi-source biological data based on a meta-path, which comprises the following steps: acquiring an original data set; constructing a fusion heterogeneous network, and fusing the incidence matrixes of every two combinations of drugs, diseases and proteins and the respective similarity matrixes to obtain a heterogeneous graph; designing a multi-level association path based on meta-path and similarity negative sampling, fusing similarity information of same-level networks in the heterogeneous network based on a similarity principle, and selecting a negative link sample; extracting drug characteristics; constructing an SMS-MPF module, carrying out migration on the meta-paths through a heterogeneous network, extracting and fusing features among different meta-paths, and obtaining associated features of drugs and diseases; and inputting the disease phenotype similarity matrix, the drug characteristics and the associated characteristics of the drugs and the diseases into an associated prediction module to obtain final node representation. The method solves the problem that an existing method has significant defects in selection of positive and negative samples.

The invention belongs to the technical field of graph neural network models, and particularly relates to a drug-disease association prediction method based on layered negative sample sampling. According to the method, node information is iteratively updated by using a graph convolutional network and a graph attention network, and neighborhood features and neighbor interaction features in drugs and disease similar networks are comprehensively considered in combination with a disease-drug association network. For the generated sample information, the method adopts a layered structure sampling technology, and a reliable negative sample drug-disease pair is constructed by combining a PageRank algorithm and a GAT attention score; finally, the homogeneous neighborhood and heterogeneous neighborhood features are fused through comparative learning, and universal field features of drugs and diseases are obtained. In addition, case analysis is carried out on Parkinson's disease and Alzheimer's disease, and the effectiveness of the method is verified through an authoritative data set of public literatures, clinical data, DugBank and DugCentral.

The invention is suitable for the field of lncRNA and disease association prediction, and particularly provides an lncRNA-disease association prediction method based on a KAN network, and the method comprises the following steps: fusing lncRNA-disease association network information and various similarity information of lncRNA and diseases to construct a comprehensive heterogeneous network; the method comprises the following steps of: constructing a Transform feature extractor based on a multi-head attention mechanism, wherein the Transform feature extractor is used for capturing global serialization features from lncRNA and a disease node; constructing a GraphSAGE feature extractor based on a heterogeneous graph network, wherein the GraphSAGE feature extractor is used for capturing topological structure features of lncRNA and a disease node; the global serialization features and the topological structure features are fused, and comprehensive lncRNA-disease association pair feature information is obtained; and a KAN network used for predicting the association score between the lncRNA and the disease is constructed. According to the method, global sequence features are extracted in combination with Transform, heterogeneous network topology information is mined through GraphSAGE, a nonlinear relation is modeled through a KAN network, multilevel information of lncRNA and diseases is fully mined, and the accuracy of correlation prediction between lncRNA and diseases is improved.

The invention relates to the technical field of bioinformatics, and discloses a three-generation full-length transcript sequencing data-based HLA (human leukocyte antigen) typing method, system, equipment and medium, by utilizing the technical advantage of long read length of a three-generation sequencing single molecule, a complete transcript sequence of an HLA gene is directly obtained, full-length HLA typing without PCR (polymerase chain reaction) amplification and short read length splicing is realized, and the method, the system and the equipment are simple and convenient to operate. The blank of the three-generation full-field transcript in the aspect of detecting the HLA typing is filled. The method effectively solves the problem that the cost, the efficiency and the accuracy are difficult to consider in the traditional HLA typing technology, not only avoids PCR amplification preference and short read length assembly errors and remarkably improves the typing accuracy, but also reduces the analysis complexity through an optimized bioinformatics process. According to the method, an efficient solution with economical efficiency and reliability can be provided for application scenes such as clinical transplantation matching and disease association research, and the method has important clinical application value and wide market prospects.

The invention belongs to the technical field of drug and disease association prediction, and particularly relates to a drug and disease association prediction method based on a hyperbolic graph feature learning network, and the method comprises the following specific steps: S1, obtaining drug and disease association data from a database, and constructing a drug similarity matrix DR and a disease similarity matrix DS, the features are extracted and then mapped to a hyperbolic space, and a hyperbolic drug initial feature matrix ZRH and a hyperbolic disease initial feature matrix ZDH are obtained and used for constructing a drug-disease adjacency matrix A; according to the method, similar features and heterogeneous features are effectively extracted through the hyperbolic graph feature reconstructor and the hyperbolic heterogeneous variable graph converter, and the accuracy of the features is improved; in addition, similar features and heterogeneous features are effectively fused through the hyperbolic collaborative representation learning strategy, and the comprehensiveness of the features is improved; in addition, the positive and negative fusion difficulty sampling strategy synthesizes the most informative negative sample, and can better distinguish the positive and negative sample pairs.

The invention provides an LncRNA-disease association prediction method based on comparative learning. Comprising the following steps: S1, preparing an LncRNA-disease incidence matrix, calculating functional similarity, cosine similarity and Gaussian interaction spectrum kernel similarity of lncRNA, and calculating semantic similarity, cosine similarity and Gaussian interaction spectrum kernel similarity of diseases; s2, constructing an LncRNA similarity graph, a disease similarity graph and an LncRNA disease association graph; s3, obtaining an LncRNA feature matrix, an LncRNA auxiliary feature matrix, a disease feature matrix and a disease auxiliary feature matrix; s4, obtaining an LncRNA personalized fusion feature and a disease personalized fusion feature; and S5, carrying out cross-view comparative learning on the personalized fusion features of the LncRNA and the personalized fusion features of the diseases, and predicting potential LncRNA and disease association. According to the method, information fused into bioinformatics is considered, so that the method has certain value for predicting the potential correlation between the LncRNA and the diseases, improving the accuracy of disease diagnosis and treatment and promoting the development of drug research and development.