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 could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA.

Results: MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively.

Conclusions: All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Keywords: Disease; Kronecker regularized least squares; Multiple kernel learning; miRNA; miRNA–disease association.

Fig. 1

Flowchart of MKRMDA model to predict potential miRNA–disease associations based on multiple kernels of miRNA and disease and known miRNA–disease associations in HMDDv2.0 database

Fig. 2

Performance comparisons between MKRMDA and some state-of-the-art disease–miRNA association prediction models (HGIMDA, RLSMDA, HDMP, sWBSMDA, MCMDA and RKNNMDA) in terms of ROC curve and AUC based on local and global LOOCV, respectively. As a result, MKRMDA achieved AUCs of 0.9040 and 0.8446 in the global and local LOOCV, which represents more outstanding prediction performance than all the previous classical models