A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs.

ABSTRACT:

Background

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.

Results

In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRN

SUBMITTER: Xiao Y 

PROVIDER: S-EPMC7709313 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature