Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L2,1$$ {L}_{2,1} $$ norm regularization terms

Abstract

Abstract Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease‐associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time‐consuming and expensive. In this study, we introduced a new method called iPALM‐GLMF, which modelled microbe–disease association prediction as a problem of non‐negative matrix factorization with graph dual regularization terms and norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non‐negative matrix factorization and to improve the interpretability. To solve the model, iPALM‐GLMF used a non‐negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM‐GLMF performed better than other existing methods in leave‐one‐out cross‐validation and fivefold cross‐validation. In addition, case studies of different diseases demonstrated that iPALM‐GLMF could effectively predict potential microbial‐disease associations. iPALM‐GLMF is publicly available at https://github.com/LiangzheZhang/iPALM‐GLMF .