Ensemble learning based on matrix completion improves microbe-disease association prediction.

ABSTRACT: Microbes have a profound impact on human health. Identifying disease-associated microbes would provide helpful guidance for drug development and disease treatment. Through an enormous experimental effort, limited disease-associated microbes have been determined. Accurate computational approaches are needed to predict potential microbe-disease associations for biomedical screening. In this study, we present an ensemble learning framework entitled SABMDA to improve microbe-disease association inference. We first integrate multi-source of information from both microbes and diseases, and develop two matrix completion algorithms to predict microbe-disease associations successively. Ablation tests show combining the two matrix completion algorithms can receive better prediction performance. More

SUBMITTER: Chen H 

PROVIDER: S-EPMC11879468 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature