A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.

ABSTRACT: A growing number of clinical observations have indicated that microbes are involved in a variety of important human diseases. It is obvious that in-depth investigation of correlations between microbes and diseases will benefit the prevention, early diagnosis, and prognosis of diseases greatly. Hence, in this paper, based on known microbe-disease associations, a prediction model called NBLPIHMDA was proposed to infer potential microbe-disease associations. Specifically, two kinds of networks including the disease similarity network and the microbe similarity network were first constructed based on the Gaussian interaction profile kernel similarity. The bidirectional label propagation was then applied on these two kinds of networks to predict potential microbe-disease associations. We applie

SUBMITTER: Wang L 

PROVIDER: S-EPMC6465563 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature