Graph regularized L(2,1)-nonnegative matrix factorization for miRNA-disease association prediction

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. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL(2,1)-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS: The new method (GRL(2, 1)-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL(2, 1)-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.