Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.

ABSTRACT:

Background

Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations.

Methods

In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among

SUBMITTER: Nguyen VT 

PROVIDER: S-EPMC8600685 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature