RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction.

ABSTRACT: Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph coul

SUBMITTER: Niu YW 

PROVIDER: S-EPMC6635699 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature