13 results about "Gene ontology" patented technology

The invention provides a pathogenic driver gene prediction method based on biological network fusion and related equipment. The method comprises the following steps: acquiring data of various driver genes for training; constructing an initial gene relationship map based on protein interaction, gene sequence similarity, KEGG pathway co-occurrence, a gene co-expression mode and semantic similarity of a gene ontology, and embedding various human driven gene data for training into each node in the initial gene relationship map to obtain various gene relationship maps; performing dynamic adjustment on each gene relationship map through edge discarding, feature discarding and difficult sample recognition enhancement to obtain an adjusted gene relationship map for training the constructed pathogenic driving gene prediction model to obtain a trained pathogenic driving gene prediction model; inputting the target driver gene data into the trained pathogenic driver gene prediction model for prediction to obtain a prediction result; and the accuracy and robustness of pathogenic driver gene prediction are improved.

The application provides a gene ontology guided multi-expert gene expression prediction method, comprising the following steps: S1, using a feature extraction network to extract global morphological features with spatial context information from image patch sequences; S2, based on spatial transcriptome data, using gene ontology knowledge to generate and organize a spatial resolution functional atlas corresponding to a spatial structure; S3, inputting the global morphological features obtained in S1 and the spatial resolution functional atlas obtained in S2 into a hybrid expert model; generating function-aware collaborative features; S4, integrating features for a target image patch; inputting the integrated fusion features into a prediction network to regress a predicted gene expression profile of the target image patch. The application realizes the most advanced prediction performance on multiple public datasets, and significantly improves the biological interpretability of the model.

The invention belongs to the cross technical field of computer-aided drug discovery and deep learning, and discloses a drug virtual screening method and system based on gene ontology enhanced contrast learning, and the method comprises the steps: collecting a protein-small molecule historical pairing data set; constructing a multi-mode dual-encoder framework which is composed of a protein encoder and a small molecule encoder and supports sequence and structure dual-mode input; introducing gene ontology information, and performing functional semantic enhancement on the protein encoder through protein-gene ontology and gene ontology-gene ontology contrast learning tasks; a model is trained through the combination of comparative learning and affinity sorting; in the reasoning stage, protein representation independent of gene ontology information is used for small molecule screening, multi-modal prediction results are integrated through an uncertainty perception fusion mechanism, and a more stable sequencing result is obtained. The generalization ability of the model to unknown proteins is improved, and the method is suitable for actual drug discovery scenes.

The invention belongs to the technical field of enzyme catalytic conversion number prediction, and discloses an enzyme catalytic conversion number prediction method and system based on function annotation and a hierarchical structure, and the prediction method comprises the steps: extracting protein unique identifier information based on a protein resource database, analyzing protein dynamic information and gene ontology functions according to the unique identifier information, and obtaining a prediction result; obtaining a relation table of the gene ontology and the enzymatic conversion coefficient; combining the hierarchical structure of the gene ontology with the relation table, capturing the mutual relation between the gene and the gene product, and constructing a hierarchical total tree between the gene ontology and the enzymatic conversion coefficient according to a relation capturing result; and extracting target gene ontology information according to the gene code of the target object, and matching an enzymatic conversion coefficient corresponding to the target gene ontology information from the hierarchical total tree as an enzymatic conversion number prediction result. According to the invention, gene and protein function annotations are provided by using the gene ontology, so that the function similarity of enzymes can be measured based on the gene ontology, and the enzymatic conversion value of unknown enzymes is speculated.

The invention discloses a protein function prediction method combining PO2GO and an attention mechanism, and belongs to the field of protein function prediction.The protein function prediction method comprises the steps that a protein sequence is obtained, the protein sequence is coded, mean pooling processing is conducted on a coding result, and sequence features of protein are obtained; transforming the sequence features of the protein through a machine learning model to obtain optimized features; converting the optimized features through a self-attention mechanism to obtain protein features; obtaining an embedded representation of a gene ontology term, and processing the embedded representation of the gene ontology through a machine learning model to obtain a gene ontology term feature; according to the method, the protein features and the gene ontology term features are fused to obtain weighted features, the protein features and the weighted features are spliced, the splicing result is predicted through a machine learning method to obtain a protein function prediction result, and the far-source homologous protein prediction accuracy can be improved.

The invention relates to a drug-target affinity prediction method and system based on gene ontology guidance and multi-modal attention. The method comprises the following steps: complementing gene ontology GO function annotation of target protein; gO feature representation is extracted, sequence features are extracted through an ESM-2 model, and fusion is carried out through a gating mechanism to obtain protein features; using a Molformer model to obtain drug sequence features, converting an SMILES sequence of a drug molecule into a graph structure, using Transformer to extract topological structure features, and fusing to obtain drug features; a double-branch multi-head cross attention mechanism is used to realize bidirectional interaction of protein features and drug features, a Mama layer is used to perform deep feature extraction, and then prediction is performed. By extracting protein features and drug features, complex interaction between a drug and a target spot can be effectively captured; and a head cross attention mechanism is combined with a Mama layer to carry out deep feature extraction, so that the feature expression capability and the prediction performance are enhanced.

The present application belongs to the field of biotechnology, and specifically relates to a method for high-throughput screening of nucleic acid drug target genes, which comprises the following steps: treating cells with nucleic acid drug candidates or negative controls, and collecting measurement values for a plurality of cell characteristic parameters related to the treatment purpose of the nucleic acid drug; calculating the fold change of the characteristic parameter measurement values of the cells in each candidate hole relative to the negative control measurement values and normalizing them; for all measurement values, calculating the squared Mahalanobis distance of the plurality of cell characteristic parameters relative to the average value using the normalized values; screening out the candidates with squared Mahalanobis distance greater than a cutoff value, and unsupervised clustering the cells according to the plurality of cell characteristic parameters to divide the candidates into at least one cluster; obtaining the corresponding silenced genes of the candidates in each cluster, and performing gene ontology enrichment analysis on the genes in the cluster to obtain the biological processes associated with each cluster, and finally obtaining nucleic acid drug target genes for different biological processes.

The application belongs to the technical field of biological medicine, and provides application of gene PTHLH as a target point in auxiliary diagnosis, prognosis judgment and treatment of esophageal squamous carcinoma. The target point PTHLH is an esophageal squamous carcinoma driving target point accumulated based on structural variation TD, that is, a potential target point of a super enhancer accumulated by TD; the super enhancer is an upstream regulation region of PTHLH. The application research finds that the enhancer region of the ESCC driving gene is frequently amplified by TD. These results suggest that the TD event can regulate the related expression of the cancer gene by affecting the gene ontology and the regulation element. Meanwhile, the esophageal squamous carcinoma driving target point accumulated based on the structural variation TD is found to be the potential target point PTHLH of the super enhancer accumulated by TD.

The application provides serum biomarkers for diagnosing and evaluating the activity of aortitis and application, and relates to the field of disease activity evaluation. The serum biomarkers are serum proteins: HP and ORM1. HP and ORM1 are determined as two new serum biomarkers through comprehensive proteomics analysis; gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis are carried out using a DAVID database; a protein-protein interaction network is constructed using Metascape. A random forest model is trained using a caret package, and a nomogram is visualized using an rms package. Serum samples are obtained, and the serum levels of HP and ORM1 are determined using a commercial ELISA kit; they are significantly related to disease activity, and are involved in inflammation / immune processes in aortitis, so the markers have the potential to serve as an auxiliary tool for monitoring the activity of aortitis.

This invention proposes a method and apparatus for protein function prediction based on a topology-aware attention network. The method includes the following steps: S1: acquiring a publicly available protein function annotation dataset, a publicly available gene ontology description, and relational .obo files; S2: constructing a protein graph input and a gene ontology graph input; S3: building a topology-aware protein-gene ontology attention network, including a topology-aware attention module and a multi-head aggregator; S4: training the topology-aware protein-gene ontology attention network from step S3 using the data from step S1 to obtain a trained protein function prediction model; S5: for a protein whose function is to be predicted, using the trained model from step S5, generating the probability that it is annotated by each GO term. This method can improve the performance of protein function prediction.

The application discloses a kind of construction methods of glaucoma genetic information database, it is related to database technical field, and its technical solution key points include the following steps: through literature retrieval and database query, the genetic information of glaucoma related gene is collected;Through high-throughput gene sequencing, sequencing result obtained by genome sequencing of glaucoma patient;The sequencing result is analyzed by bioinformatics and the mutation variation data related to glaucoma is screened out;Mutation variation data and genetic information form preliminary gene dataset;Based on gene dataset, the rear end architecture of database is constructed using database management system, and the front-end website platform with data retrieval function is configured;Gene basic information query, gene ontology enrichment analysis and protein interaction network analysis on gene dataset are integrated in front-end website platform;Effect is to promote the fusion development of glaucoma genetic research and clinical practice.

The present invention discloses a method for mining protein functional modules, a computer device, and a computer-readable storage medium. Based on the node-level adaptive graph convolutional network (NASGC) model, through an adaptive mechanism, each protein node separately learns high-order and low-order neighbor information. In the vector representation information of the protein nodes obtained by learning, high-order and low-order structural information is fused on the gene ontology attribute features of the protein nodes, resulting in a more generalized protein node representation. Thus, protein complexes and protein signaling pathways can be mined from the protein interaction network, and the generated protein complexes are more in line with the actual situation, improving the accuracy of protein function recognition.

The invention discloses an IncRNA function prediction method based on gene ontology structure semantic perception, and relates to an IncRNA function prediction method. In order to solve the problems that according to an existing lncRNA function prediction method, fusion of multi-source heterogeneous information is insufficient, a DAG structure of a GO body is difficult to use at the same time, and a prediction result violates a True Path Rule, firstly, a VAE-based method is used for effectively fusing multi-source heterogeneous features of lncRNA; the method comprises the following steps of: firstly, extracting semantic features of lncRNA by using a BioBERT method, finally, injecting lncRNA information into GO features through cross attention, adopting DAG-LSTM to bidirectionally spread the features, and finally, enabling GO to pay more attention to True Path Rule information by using a self-attention mechanism causal mask. According to the invention, the accuracy of lncRNA function prediction can be effectively improved.