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

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. Moreover, comprehensive experiments, including cross-validations and independent test, demonstrate that SABMDA outperforms seven recent baseline methods significantly. Finally, we apply SABMDA to three diseases to predict their associated microbes, and results show SABMDA's remarkable prediction ability in real situations.