MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

ABSTRACT:

Background

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity.

Methods

In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which

SUBMITTER: Chen X 

PROVIDER: S-EPMC5727873 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature