Graph regularized L_2,1-nonnegative matrix factorization for miRNA-disease association prediction.

ABSTRACT:

Background

The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.

Results

Here, we present a computational framework based on graph Laplacian regularized L_{2, 1}-nonnegative matrix factorization (GRL_{2, 1}-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs.

SUBMITTER: Gao Z 

PROVIDER: S-EPMC7029547 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature