Semi-supervised learning for potential human microRNA-disease associations inference

Abstract

MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

“…We implemented local and global LOOCV to evaluate the prediction accuracy of NDAMDA and 6 previous computational models: WBSMDA,20 RLSMDA,24 MCMDA,28 HDMP,21 RWRMDA 19 and MiRAI 22. In LOOCV, each known association was used as the validation sample and the remaining known associations were regarded as the training samples.…”

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

“…Not only that, three computational models: WBSMDA [4], RLSMDA [12], and NCPMDA [56], were introduced to compare the prediction performance with CMFMDA. To obtain relevant miRNA information for the chosen disease , all association related to disease was left out, and the rest of the associations serve as a training set to get prediction association by CMFMDA.…”

“…Since the first two miRNA lin-4 and let-7 were found in 1993 and 2000 [10,11], thousands of miRNAs have been detected in eukaryotic organisms ranging from nematodes to humans. The latest venison of miRBase contains 26845 entries and more than 2000 miRNAs have been detected in human [12][13][14]. With the development of bioinformatics and the progress of miRNA-related projects, researches are gradually focused on the function of miRNAs.…”

“…Existing studies have shown that miRNAs are involved in many important biological processes [15,16], like cell differentiation [17], proliferation [18], signal transduction [19], viral infection [20], and so on. Therefore, it is easy to find that miRNAs have close relationship with various human complex diseases [12,[21][22][23][24][25][26]. For example, researchers found that mir-433 is upregulated in gastric carcinoma by regulating the expression of GRB2, which is a known tumour-associated protein [27].…”

“…However, using experimental methods to identify miRNA-disease association is expensive and time-consuming. As the miRNA-related theories are becoming more and more common, such as the prediction model about miRNA and disease, the function of miRNA in biological processes, and signaling pathways, new therapies are urgently needed for the treatment of complex disease; it is necessary to develop powerful computational methods to reveal potential miRNA-disease associations [12,15,20,[34][35][36][37][38][39][40].…”