LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction.

ABSTRACT: Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs' potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases' statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of

SUBMITTER: Chen X 

PROVIDER: S-EPMC5749861 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature