WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network.

ABSTRACT:

Background

An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases.

Results

Here, we developed a novel computational model to pre

SUBMITTER: Long Y 

PROVIDER: S-EPMC6824056 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature