Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes - Xu, Da; Xu, Hanxiao; Zhang, Yusen; Gao, Rui (Frontiers in Microbiology, 2022)
Extensive clinical and biomedical researches have shown 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 nonnegative matrix factorization technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted nonnegative matrix factorization technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least squares method for prediction. The experimental results of 5-fold and leave-one-out cross-validation demonstrated our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark data set. Case studies further showed proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.