ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction

Abstract

Recently, accumulating evidences have indicated miRNAs play critical roles in the progression and development of various human complex diseases, which pointed out that identifying miRNA-disease association could enable us to understand diseases at miRNA level. Thus, revealing more and more potential miRNA-disease associations is a vital topic in biomedical domain. However, it will be extremely expensive and time-consuming if we examine all the possible miRNA-disease pairs. Therefore, more accurate and efficient methods are being highly requested to detect potential miRNA-disease associations. In this study, we developed a computational model of Ensemble Learning and Link Prediction for miRNA-Disease Association prediction (ELLPMDA) to achieve this goal. By integrating miRNA functional similarity, disease semantic similarity, miRNA-disease association and Gaussian profile kernel similarity for miRNAs and diseases, we constructed a similarity network and utilized ensemble learning to combine rank results given by three classic similarity-based algorithms. To evaluate the performance of ELLPMDA, we exploited global and local Leave-One-Out Cross Validation (LOOCV), 5-fold Cross Validation (CV) and three kinds of case studies. As a result, the AUCs of ELLPMDA is 0.9181, 0.8181 and 0.9193+/-0.0002 in global LOOCV, local LOOCV and 5-fold CV, respectively, which significantly exceed almost all the previous methods. Moreover, in three distinct kinds of case studies for Kidney Neoplasms, Lymphoma, Prostate Neoplasms, Colon Neoplasms and Esophageal Neoplasms, 88%, 92%, 86%, 98% and 98% out of the top 50 predicted miRNAs has been confirmed, respectively. Besides, ELLPMDA is based on global similarity measure and applicable to new diseases without any known related miRNAs.

Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides

“…Based on laplacian regularized sparse subspace learning, extreme gradient boosting machine, and ensemble learning, respectively, the computational models developed by Chen X et al . achieved superior prediction accuracy for miRNA-disease association 29 – 31 . Based on ensemble rotation forest learning, Wang L et al .…”

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

“…In line with the manually verified microRNA-disease link network, microRNA similarities network and disease similarities network, KATZ integrates the social network analysis approach with machine learning. Chen et al [44] inferred disease-related miRNAs based on ensemble learning and link prediction (ELLPMDA). According to global similarity measures, ELLPMDA uses ensemble learning for integrating ranking results, which were obtained via three typical similarity-measurement approaches.…”

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

“…In the graph, potential association between a miRNA‐disease pair could be inferred from an iterative equation. In 2018, Chen et al put forward a novel calculation method of Ensemble Learning and Link Prediction for miRNA‐Disease Association prediction (ELLPMDA), in which they gained final scores for the novel miRNA‐disease associations through weighted combining the three outcomes obtained from common neighbours, Jaccard index and Katz index, respectively. In the same year, Chen et al further introduced a model of Inductive Matrix Completion for MiRNA‐Disease Association prediction (IMCMDA) through implementing the low‐rank inductive matrix completion method on the basis of the data set of known miRNA‐disease associations, miRNA similarity and disease similarity.…”

“…There are many computational methods proposed to predict the potential associations between miRNAs and diseases, most of which are developed based on the assumption that miRNAs with similar functions are more likely to have connections with diseases of similar phenotypes. [16][17][18][19][20][21] Every time a new model was proposed, the prediction accuracy would be increased. In 2010, a hypergeometric distribution-based model was presented by Jiang et al 22 to predict miRNA-disease associations, where disease phenotype similarity, miRNA functional similarity and known human disease-miRNA associations were integrated.…”