{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xuan P"],"funding":["Natural Science Foundation of China","Guangdong Basic and Applied Basic Research Foundation","STU Scientific Research Initiation Grant"],"pagination":["231"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12465207"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(1)"],"pubmed_abstract":["Background
The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the functional mechanisms of drugs. Recently, the graph learning based methods demonstrated their advantages in extracting the node features from the biological heterogeneous graphs. However, the previous methods failed to completely preserve the intrinsic structures of biological data and did not fully utilize the topological and positional information for predicting the drug-microbe associations.Results
We propose a new prediction model, structure-sensitive transformer and multi-view graph contrastive learning for microbe-drug association prediction (SMMDA), to "],"journal":["BMC bioinformatics"],"pubmed_title":["Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes."],"pmcid":["PMC12465207"],"funding_grant_id":["2024A1515010176","62372282, 62172143","NTF22032"],"pubmed_authors":["Xuan P","Zhang T","Wang R","Cui H","Gu J"],"additional_accession":[]},"is_claimable":false,"name":"Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes.","description":"Background
The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the functional mechanisms of drugs. Recently, the graph learning based methods demonstrated their advantages in extracting the node features from the biological heterogeneous graphs. However, the previous methods failed to completely preserve the intrinsic structures of biological data and did not fully utilize the topological and positional information for predicting the drug-microbe associations.Results
We propose a new prediction model, structure-sensitive transformer and multi-view graph contrastive learning for microbe-drug association prediction (SMMDA), to ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-02T03:13:33.569Z","creation":"2026-05-02T03:08:17.049Z"},"accession":"S-EPMC12465207","cross_references":{"pubmed":["41013188"],"doi":["10.1186/s12859-025-06199-w"]}}