A random forest based computational model for predicting novel lncRNA-disease associations.

ABSTRACT:

Background

Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.

Results

To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and

SUBMITTER: Yao D 

PROVIDER: S-EPMC7099795 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature