A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

ABSTRACT: Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model na

SUBMITTER: Pan J 

PROVIDER: S-EPMC10981679 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature