Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.

ABSTRACT: Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe-disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with m

SUBMITTER: Xu D 

PROVIDER: S-EPMC8965656 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature