FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

ABSTRACT:

Background

Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.

Methods

In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association sco

SUBMITTER: Li X 

PROVIDER: S-EPMC6449885 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature