126 results about "Disease Association" patented technology

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 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 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 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 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 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 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 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 discloses a drug-disease association prediction method, system, device and medium, and relates to the technical field of drug relocalization, and the method comprises the steps: constructing a drug similarity network and a disease similarity network; obtaining a binary adjacency matrix through k-neighbor graphs of different nodes in the two similarity networks, carrying out weighted fusion on the binary adjacency matrix through an attention coefficient to obtain a soft adjacency matrix, and carrying out fine-grained graph convolution updating to obtain drug features and disease features; extracting heterogeneous node representations of drugs and diseases in the biochemical heterogeneous network, and carrying out dynamic weight distribution on different representations to obtain final embedding of drug nodes and disease nodes; and splicing the final embedding of the drug nodes and the final embedding of the disease nodes, and performing prediction according to the spliced features to obtain the drug-disease association probability. According to the method, the potential complementary relationship between the two is fully mined, and the heterogeneous feature fusion effect is improved, so that the performance and robustness of drug-disease association prediction are integrally enhanced.

The present invention discloses a method for predicting drug-drug interactions based on secure multi-party computing, comprising the following steps: S1, obtaining a drug-drug interaction network, a drug-protein interaction network, a drug-disease association network, and a drug-side effect association network; S2, calculating Jaccard similarity based on different drug features to obtain similar features between all drugs, and using principal component analysis technology to reduce the dimensionality of all drug similarity features; S3, dividing each user's private feature data into four parts, encrypting them using secret sharing technology, and sending them to four servers using an additional secret sharing mechanism; S4, inputting the four parts of feature data into a preset private deep learning model to predict drug-drug interactions. The present invention enables high-quality collaboration between pharmaceutical companies and research institutions without leaking drug privacy information, thereby improving drug-drug interaction prediction.

The invention relates to a disease-specific quantitative trait locus identification method based on multi-omics integration. The method comprises the following steps: acquiring variation sites of whole genome sequencing data of a target object, and molecular phenotypes and molecular abundance of molecular phenotype data; determining an association significance probability value of an association pair formed by the variation point and the molecular phenotype based on the variation point and the molecular abundance, and screening a first association pair from the association pair based on the association significance probability value and condition analysis; determining a consistent second association pair in the normal association pair and the disease association pair, and determining a third association pair with a disease interaction effect in the second association pair; calculating a first effect estimation value and a second effect estimation value of each third association pair; and based on the first effect estimation value and the second effect estimation value of the third correlation pair, determining a target correlation pair related to the Parkinson's disease, and taking the target correlation pair as the identified quantitative trait site. By adopting the method, the Parkinson's specific pathogenic heritable variation can be accurately identified.

The application discloses a drug recommendation method and system based on drug mapping and diagnosis enhancement, the natural language processing technical field and the recommendation system field, aiming at the problems of implicit drug and disease association, single diagnosis information and poor interpretability in the existing drug recommendation technology, and realizes accurate recommendation through four core modules: a diagnosis enhancement module utilizes a large language model to associate diagnosis and treatment methods, physical examination, and generates enhanced diagnosis information; a drug mapping module establishes an explicit association of drugs, diagnosis and treatment methods, a drug recommender module combines a cross-attention mechanism and a graph neural network, and respectively models enhanced diagnosis representation and drug external knowledge; and a joint training module balances single-disease and global recommendation loss to optimize model performance. The application is superior to existing mainstream models in various indicators, can significantly improve the accuracy, clinical interpretability and safety of drug recommendation, and is suitable for assisting doctors in formulating personalized medication schemes.

The invention discloses an accurate report automatic analysis and generation system based on gene detection data, and belongs to the technical field of bioinformatics and artificial intelligence. The system comprises a variation intelligent labeling module, a heterogeneous knowledge graph reasoning module, a scene adaptive report generation module and an intelligent quality control and feedback module; a three-layer heterogeneous knowledge graph containing gene-disease association, drug-gene interaction and clinical guide decision is constructed, variation-disease association reasoning is performed by adopting a relational graph attention network, scene adaptive generation of report content is realized in combination with a semantic slot filling mechanism, and the four modules form a closed-loop collaborative system through deep coupling. Gene detection data can be automatically converted into a clinical diagnosis report, and the report generation time is shortened from 48 hours to 30 minutes or less.

The invention discloses an lncRNA and disease association prediction method based on multitask graph attention and information completion, and belongs to the field of data mining in bioinformatics. The method comprises the following steps: firstly, according to an lncRNA interaction spectrum set, a miRNA interaction spectrum set and MeSH description of a disease, respectively calculating to obtain an interaction spectrum similar matrix SL of the lncRNA, an interaction spectrum similar matrix SM of the miRNA and a semantic similar matrix SD of the disease; secondly, the similar information is combined with LMA, LDA and MDA to construct an lncRNA-miRNA heterogeneous network, an lncRNA-disease heterogeneous network and a miRNA-disease heterogeneous network; then, internal representation of similar information is obtained through a GCN to serve as initial embedding, high-order topological information of interaction nodes is extracted through a graph attention Unet model, and an incidence matrix is reconstructed through a bilinear decoder; and finally, constructing a multi-task learning framework, and realizing association prediction through an information completion method. Through experiments, the powerful performance of predicting potential correlation between the lncRNA and the disease by the model is effectively verified.

The invention discloses a disease analysis system based on an improved hyperbolic graph neural network model, which combines hyperbolic space embedding, high-order spectral filtering and an adaptive mechanism to improve the expression and reasoning ability of multi-scale structural features in a medical knowledge graph. The system can extract richer structural features in a frequency domain by introducing a high-order spectrum filter constructed based on a Legendre polynomial; automatically adjusting the receptive field range of the filter to adapt to different graph structures by using a self-adaptive order; and disease node classification and relation prediction are realized through logarithm mapping and a neural classifier. Experimental results show that compared with an existing method, the method has higher accuracy and lower loss value in disease map node classification and link prediction tasks, a structured medical knowledge system can be optimized, medical map information can be complemented, potential disease association and drug connection can be found, and the method has good clinical application prospects.

The invention belongs to the technical field of bioinformatics, and particularly relates to an lncRNA and disease association prediction method based on deep learning, which comprises the following specific steps: firstly, according to known lncRNA-disease association information, disease-miRNA association information and lncRNA-miRNA association information, constructing an lncRNA-disease association matrix, a disease-miRNA association matrix and an lncRNA-miRNA association matrix; and constructing an lncRNA comprehensive similarity matrix LS, a disease comprehensive similarity matrix DS and a miRNA comprehensive similarity matrix MS. The biological sequence selective compression network adopted by the invention can effectively model a long-distance dependency relationship in an ultra-long sequence and dynamically distinguish key functional fragments from redundant fragments according to context.

The invention provides a tuberculosis prediction method and system based on multi-disease association dynamic fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source disease data which comprises tuberculosis data and associated disease data; preprocessing the multi-source disease data to obtain a data set; dividing the data set into a training set and a verification set; using the training set to train a plurality of prediction models; calculating performance indexes of the plurality of prediction models by using the verification set; calculating dynamic weights of the plurality of prediction models based on the performance indexes; and performing weighted fusion on the prediction results of the plurality of prediction models by using the dynamic weight to obtain a fused prediction result. According to the invention, by fusing the associated disease data, a multi-model dynamic fusion prediction system is constructed, the tuberculosis prediction precision is significantly improved, the method has strong robustness, multi-scene adaptability and visual decision support capability, and accurate prevention and control of public health are effectively assisted.

The present invention belongs to the technical field of traditional Chinese medicine assisted diagnosis and treatment, and specifically discloses an assisted diagnosis and treatment system based on a traditional Chinese medicine large model. By receiving tongue image uploaded by patients at the same time period regularly, tongue image features of different tongue body regions are extracted. Thus, by using the tongue image features under time series, tongue image change curves are generated, and the syndrome type evolution path is output by using the tongue image - syndrome type model. Then, the clinical indicators of the patients in the same time series are associated with the syndrome type evolution path to form a syndrome type - disease association matrix. Based on this, a prescription compatibility scheme is recommended to ensure that the treatment plan is not only based on the syndrome type, but also fully considers the specific pathological mechanism and clinical manifestations, avoiding the disconnection between the treatment plan and the actual condition, realizing more accurate and personalized traditional Chinese medicine assisted diagnosis and treatment. In addition, automated data processing and analysis reduce manual intervention, improve the diagnosis and treatment efficiency, and save medical resources.

The application discloses an RNA-disease correlation relationship visual analysis method based on a density relationship graph, first acquires internal and external databases, and establishes a corresponding relationship between a prediction result and an internal RNA-disease correlation relationship database; a graph data structure is established, the acceleration and speed of a node at a current time are simulated through discrete force, the position of the node at a next time is calculated, the weighted density of the node on a two-dimensional plane is estimated, and the density contour lines of each density level are calculated; each heterogeneous node and different markers are drawn to distinguish the biological semantic features of the nodes, and the correlation relationship between the nodes is drawn based on edge coordinate layout; a Voronoi polygon graph on the two-dimensional plane and a corresponding quadtree spatial index are calculated to determine the interactive object when a mouse is moved. The application can not only help biological researchers to efficiently check and explore the existing RNA-disease correlation relationship, but also support a user in verifying and analyzing the prediction result of a deep learning model.

The application relates to the technical field of biosciences, and is a circRNA-disease correlation prediction model construction method and correlation prediction method based on dynamic contrast sampling linear and nonlinear feature resonance and related devices, which comprises the following steps: obtaining a training set and a test set; training a multi-view contrast learning DCFR-CDA model by using the training set; introducing a loss function during the training; ending the training when the value of the loss function is stable; obtaining a circRNA-disease correlation prediction model; testing the circRNA-disease correlation prediction model by using the test set; optimizing model parameters; and outputting a circRNA-disease correlation prediction model meeting test evaluation requirements. The application jointly applies a linear decomposition method and nonlinear decomposition methods of multi-layer perception decomposition and one-dimensional convolution decomposition to generate multi-view embedding compatible with global and local modes, thereby effectively improving the prediction accuracy of the model.

The invention discloses a microRNA and disease association prediction method and device, equipment and a medium, and relates to the technical field of data processing. The method comprises the steps that a node pair semantic graph is constructed based on a microRNA and disease heterogeneous graph, the microRNA and disease heterogeneous graph represents the incidence relation between different microRNA nodes and different disease nodes, the node pair semantic graph represents the adjacency relation between all node pairs, and one node pair comprises one microRNA node and one disease node; determining semantic embedding of all node pairs according to the node pair semantic graph; constructing a specified order neighborhood sub-graph of each node pair on the basis of the microRNA and the disease heterogeneous graph, and determining topological embedding of all the node pairs on the basis of each specified order neighborhood sub-graph; and according to semantic embedding and topological embedding, predicting the association result of the microRNA and the disease so as to improve the accuracy and comprehensiveness of the prediction result.

The invention discloses a miRNA-disease association prediction method based on a heterogeneous graph redundancy elimination perception network, which comprises the following steps of: firstly, integrating similarity and association data to construct a heterogeneous graph, and projecting node features to the same space; the features and topological information are input into a model, the features are decoupled through an encoder, a redundancy suppression mechanism is used for decorrelation, and bidirectional cross attention and a high-order interaction mechanism are introduced for information fusion; according to the method, experiments show that the indexes such as the AUC and the AUPR of the prediction method on an HMDDv2.0 data set are superior to those of an existing model.

The application provides a circular RNA-disease association prediction method and system based on multi-view hypergraph contrast learning and uncertainty perception double-branch fusion, and relates to the technical field of disease association prediction. The method comprises the following steps: constructing multi-source similarity information and a heterogeneous graph based on a circular RNA-disease association matrix; using a multi-view hypergraph contrast embedding module, capturing high-order structures through hypergraph convolution and introducing cross-view contrast constraints to obtain circular RNA node embedding and disease node embedding, and independently modeling through a semantic propagation branch and a local structure branch to obtain two prediction scores; and fusing the double-branch results through a sample adaptive gating fusion and uncertainty perception correction module, quantifying prediction divergence, and obtaining a final prediction score. The application enhances node representation consistency through multi-view hypergraph contrast learning, improves prediction robustness by combining uncertainty perception correction, and can effectively identify potential circular RNA-disease associations.

The application discloses a drug repositioning 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 field knowledge heterogeneous graph; based on the biomedical field knowledge heterogeneous graph, generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair; by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network, generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair; and based on the disease knowledge embedding, the drug knowledge embedding, the disease structure embedding and the drug structure embedding, obtaining a drug repositioning result. The application accurately captures complex biological network characteristics and finely models complex entity information through an innovative drug repositioning model, so that accurate drug repositioning is realized.

The application is suitable for the technical field of bioinformatics, and provides a phosphorylation site and disease association prediction method based on a graph neural network, comprising the following steps: constructing a multi-view of phosphorylation sites and diseases based on multi-source biological data; constructing a multi-view heterogeneous graph based on a constructed phosphorylation site sequence similarity matrix and a disease semantic similarity matrix combined with site-disease association information; encoding the constructed view heterogeneous graph through a graph layer attention mechanism to generate a site and disease representation; and optimizing the site and disease representation using a contrast learning method and performing prediction. The application significantly improves prediction accuracy, solves problems such as insufficient information utilization, weak feature expression capability and over-smoothing of deep graph neural networks in existing methods.

The invention belongs to the field of bioinformatics, and relates to a circRNA-disease association prediction method based on an attention fusion graph-hypergraph convolutional network. The method comprises the following steps: firstly, constructing a circRNA-disease incidence matrix and a plurality of similarity matrixes based on a database; secondly, respectively constructing a graph adjacent matrix and a hypergraph adjacent matrix based on the similarity matrix, extracting circRNA and disease low-order local features by using a graph convolutional network, and extracting circRNA and disease high-order global features by using a hypergraph convolutional network; then, dynamically fusing circRNA and disease low-order local features obtained from the graph convolutional network and circRNA and disease high-order global features obtained from the hypergraph convolutional network through an attention aggregation mechanism, and enhancing feature interaction by means of comparative learning; then, using a variational auto-encoder to extract circRNA and disease nonlinear characteristics from the incidence matrix; and finally, integrating a plurality of circRNAs and disease characteristics, and predicting a circRNA-disease association score. According to the method, multi-level features can be effectively fused, and the prediction accuracy and robustness are improved.