Predicting Microbe-disease Association Based on Multiple Similarities and LINE Algorithm.

Numerous microbes have been found to have vital impacts on human health through affecting biological processes. Therefore, exploring potential associations between microbes and diseases will promote the understanding and diagnosis of diseases. In this study, we present a novel computational model, named MSLINE, to infer potential microbe-disease associations by integrating Multiple Similarities and Large-scale Information Network Embedding (LINE) based on known associations. Specifically, on the basis of known microbe-disease associations from the Human Microbe-Disease Association Database, we first increase the known associations by collecting proven associations from existing literatures. We then construct a microbe-disease heterogeneous network (MDHN) by integrating known associations and multiple similarities (including Gaussian interaction profile kernel similarity, microbe function similarity, disease semantic similarity and disease-symptom similarity). After that, we implement random walk and LINE algorithm on MDHN to learn its structure information. Finally, we score the microbe-disease associations according to the structure information for every nodes. In the Leave-one-out cross validation and 5-fold cross validation, MSLINE performs better compared to other existing methods. Moreover, case studies of different diseases proved that MSLINE could predict the potential microbe-disease associations efficiently.