GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.

ABSTRACT: Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes

SUBMITTER: Kuang H 

PROVIDER: S-EPMC11379827 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature