Integrating random walk and binary regression to identify novel miRNA-disease association.

ABSTRACT:

Background

In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.

Results

In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease ass

SUBMITTER: Niu YW 

PROVIDER: S-EPMC6350368 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature