74 results about "Disease Association" patented technology

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 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 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 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 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 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.

The invention discloses a map construction method and system based on traditional Chinese medicine knowledge. Semantic analysis is combined with a traditional Chinese medicine term dictionary to extract a core entity, excavate a traditional Chinese medicine major disease association and prescription compatibility relationship, construct a knowledge network and define entity attributes and a hierarchical structure, and form a traditional Chinese medicine knowledge model; basic theories and clinical schemes are further integrated and expanded to the acupoint conditioning relation, the consistency is judged, the map visual rendering and intelligent retrieval functions are achieved, and finally the treatment scheme is determined and optimized by fusing clinical requirements. According to the invention, systematicness and availability of traditional Chinese medicine knowledge are significantly improved, clinical diagnosis and treatment efficiency and accuracy are promoted, and intelligent auxiliary decision making is realized.

The invention relates to the technical field of information retrieval, and provides a pharmacological analysis system for traditional Chinese medicine prescriptions, the system comprises a multi-omics feature fusion subsystem, a multi-omics data set is subjected to feature extraction and dimension reduction operation, and a fused multi-omics feature vector is obtained; the multi-omics feature vector captures the comprehensive influence of the traditional Chinese medicine prescription on genome, proteome and metabolome levels; performing clustering and community detection on a component-target dynamic interaction network of the component collaborative network construction subsystem to obtain a component collaborative sub-network; a prescription-disease association map of the disease association path discovery subsystem is subjected to path mining to obtain a potential disease association path set; a novel prescription-disease association path is obtained; and the traditional Chinese medicine prescription optimization candidate schemes of the prescription optimization decision generation subsystem are subjected to multi-objective optimization to obtain a traditional Chinese medicine prescription optimization decision set. According to the invention, full-chain correlation analysis of chemical components, action targets, biological pathways and disease treatment of traditional Chinese medicine prescriptions is realized.

The invention relates to the technical field of medical education and biological information visualization, in particular to a disease mechanism-associated protein structure and function visualization teaching virtual simulation system, which comprises a data storage module, a structure calling visualization module, a function-disease association module, an interactive operation module and a teaching evaluation module, all the modules interact through data interfaces. According to the protein structure and function visual teaching virtual simulation system associated with the disease mechanism, deep association of a protein three-dimensional structure, a function mechanism and a disease case is realized for the first time, the knowledge splitting barrier of traditional teaching is broken, and students are helped to construct a complete cognitive chain; autonomous operation and mutation simulation are supported, an abstract structure function relation is converted into an interactive visual model, the learning difficulty is remarkably reduced, and the knowledge absorption rate is increased; an experiment task and evaluation system is built in, a teaching closed loop of preview-operation-assessment-feedback is achieved, and the requirement for large-scale medical talent training is met.

The application provides a multi-disease correlation propagation situation prediction method and system based on a graph network, relates to the technical field of disease propagation situation prediction, and comprises the following steps: collecting multi-source monitoring data and constructing a ternary heterogeneous graph; a graph neural network prediction model is constructed; the graph neural network prediction model is trained by using training data, so that a trained graph neural network prediction model is obtained; the ternary heterogeneous graph to be predicted is input into the trained graph neural network prediction model, so that a situation prediction result of each disease node at a future time step is obtained. The application realizes accurate prediction of the multi-disease collaborative propagation effect by constructing a disease-population-environment ternary heterogeneous graph network and adopting dynamic weight modulation based on a popular situation imbalance for the comorbidity edge.

The present invention is directed to methods for mining genotype-repertoire-disease associations.

This invention discloses a method, system, device, and storage medium for predicting drug-disease associations based on cross-propagation fusion and diffusion-guided multi-scale Transformer. The method acquires multi-source drug similarity, disease similarity, and known drug-disease association data; fuses multi-source similarity networks through local neighborhood sparsification and bidirectional cross-propagation; introduces noise based on the potential diffusion process and learns denoising representations; models the local-global interaction relationship between drugs and diseases using a multi-scale Transformer encoder; and finally outputs a drug-disease association probability score for ranking candidate treatment associations. This invention can improve the robustness and predictive performance of drug-disease association prediction under multi-source biomedical data and can be used for prioritizing drug relocation candidates.

The invention belongs to the technical field of artificial intelligence, and discloses a chronic disease co-disease new risk assessment method, system, device and medium, and the method comprises the steps: employing a time sequence ternary group to model a disease new path, introducing a multi-disease co-existence coefficient to quantify the new risk, and carrying out the grading, learning disease network representation by adopting a relational graph convolutional network (R-GCN), and predicting a new risk; secondly, designing a cross-time window joint training strategy, performing hot start training on the model by using embedded representation of historical data, expanding representation information of an encoder for incremental data, fusing the representation information into a disease network, and integrating data to iteratively optimize the model; according to the method, through systematic modeling of the patient medical history data and the disease association network, the potential law of the new occurrence of the common disease is disclosed, and an analysis tool and direction guidance are provided for subsequently exploring the common disease mechanism of a specific disease and identifying a high-risk evolution mode.

This invention discloses a circRNA-disease association prediction method based on multi-source feature fusion, belonging to the field of disease-aided diagnosis technology. The method first constructs a circRNA-disease association network and extracts the GIPK functional features of the disease and the Transformer sequence features of the circRNA; then, it systematically learns the network topology embedding at three scales: microscopic, mesoscopic, and macroscopic; finally, it deeply fuses the above multi-source features and inputs them into an XGBoost classifier. This invention systematically solves the three major technical defects of existing technologies—"lack of biological semantics," "single network representation," and "inefficient feature fusion"—by introducing biological attribute features, multi-scale network structure features, and a deep fusion strategy, achieving high-precision, high-robustness, and highly biologically interpretable association prediction.

The invention discloses a miRNA-disease association prediction model construction method based on multi-view contrast learning, and the method comprises the steps: constructing a multi-source contrast view based on miRNA and disease multi-source information; key features of miRNA and diseases in the multi-source contrast view are extracted based on a context-driven neighborhood feature aggregation method; and generating a prediction result according to miRNA and key features of the disease, and performing optimization training on the model by using the prediction result. According to the method, multiple views are integrated, a multi-dimensional semantic relation is coded through an adjacent matrix, the defect that multi-source information is ignored in a traditional method is overcome, key neighbor nodes are screened based on a similarity measurement KNN algorithm, high-correlation characteristics are reserved while calculation is simplified, a clear and extensible prediction model framework is constructed, and the prediction efficiency is improved. And the method is beneficial to systematic identification of high-risk disease related miRNA key features.

The application provides a disease association named entity recognition method based on an AI large model, comprising the following steps: constructing a high-quality medical corpus, wherein the high-quality medical corpus comprises electronic medical record text data and standard medical language data corresponding to the electronic medical record text data; extracting and coding features of the electronic medical record text data and the standard medical language data corresponding thereto through a large model respectively; fine-tuning the large model through a minimization multi-modal alignment loss function according to the electronic medical record coding features and the standard medical language coding features corresponding thereto; performing unsupervised entity extraction on authoritative medical literature through the fine-tuned large model, and screening a dynamic updated entity library through rule filtering and confidence sorting threshold screening; performing initial named entity recognition on a target electronic medical record, performing multi-dimensional semantic matching on the initial entity recognition result and the entity library, and taking the matching result as a final named entity recognition result. The application has the advantages of improving data efficiency, enhancing medical field knowledge adaptability and the like.

The present application relates to the technical field of computational biology, and discloses a drug repositioning method and system based on multi-task learning and deep cross-domain, which comprises the following steps: S1. Collecting data with a "target node-drug node-disease node" ternary relationship; S2. Inputting target domain and auxiliary domain data into a feature extraction network for feature extraction; S3. Building a two-layer graph attention neural network for the target domain and the auxiliary domain respectively; S4. Fusing the enhanced deep target domain feature vector and the auxiliary domain feature vector; S5. Setting a loss function of the graph attention neural network of the target domain and the auxiliary domain, and performing multi-task learning on the graph attention neural network; and S6. Outputting a final predicted drug-disease association matrix to complete drug repositioning. The present application solves the problem that the prior art does not unify the prediction of drug-target interaction and the prediction of drug-disease association, and has the characteristics of accuracy and strong robustness.

The embodiment of the application provides a complex disease function-specific core marker construction method and related equipment, and belongs to the technical field of biological detection. The method comprises the following steps: obtaining biological knowledge data related to a target disease; extracting candidate genes associated with the target disease, target function set data, disease-associated marker set and disease transcriptomics data from the biological knowledge data; performing disease association measurement on the candidate genes according to the target function set data, the disease-associated marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene; performing screening processing on the candidate genes according to the disease association measurement data to obtain selected genes; and performing marker processing on the target disease according to the selected genes to obtain a specific function core marker of the target disease. The embodiment of the application can make the construction cost of the complex disease function-specific core marker lower and the accuracy higher.

The invention relates to a service period tunnel performance evaluation method based on intra-domain self-calibration, and the method comprises the steps: obtaining the basic attribute information of a target tunnel, and building an evaluation domain according to the basic attribute information; obtaining monitoring information in the evaluation domain, constructing an index data matrix by using the monitoring information, and endowing each piece of index data with a weight; setting a mechanism association rule, calculating a mechanism association matrix and a statistical association matrix through the mechanism association rule, and obtaining a disease association matrix based on the mechanism association matrix and the statistical association matrix; and obtaining a positive ideal solution and a negative ideal solution of each index in the evaluation domain, combining the weight with the disease incidence matrix to obtain a comprehensive covariance matrix, calculating a standard mahalanobis distance between the target tunnel and the positive and negative ideal solutions by using the comprehensive covariance matrix, and obtaining a comprehensive health score. According to the invention, adaptive and quantifiable intelligent evaluation and early warning of tunnel performance are realized.