TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

Abstract

In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

“…In the framework of local LOOCV, a disease is given in advance and then each miRNA associated with this disease is left out in turn as a test sample while the rest of miRNAs associated with the disease are set as seed samples. The only difference between global LOOCV and local LOOCV is that whether we simultaneously consider the candidates from all diseases (Chen et al, 2018a , c ). Five-fold cross validation is also implemented to verify the utility of our method.…”

Prediction of circRNA-disease associations based on inductive matrix completion

“…To assess the predictive accuracy of SIMCCDA, we performed the following method using the leave-one-out cross validation (LOOCV) framework on the known circRNA-disease associations. The reason why LOOCV is used in this study is that the current common practice in this field (prediction of lncRNA/miRNA/circRNA-disease associations) [30][31][32] is to use LOOCV to measure the performance of the model. For a disease d i , each known circRNA association corresponding to the disease was left as a test sample.…”

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

“…Methods A B C Characteristic 2010 Phenome-microRNAome network [37] ✓ Â Â Cumulative hypergeometric distribution, phenotypic similarity score 2013 bipartite network [38] ✓ ✓ Â PPI network, random walk, global perspective measures 2013 Weighted kNN [39] ✓ ✓ Â Weighted kNN, miRNA family and the cluster information 2013 MBSI [47] Â ✓ Â microRNA-based similarity inference, global network similarity measure) 2013 PBSI [47] ✓ Â Â phenotype-based similarity inference, global network similarity measure 2013 NetCBI [47] Â ✓ Â network-consistency-based inference, global network similarity measure 2014 miRNAs Prioritization [40] Â ✓ Â molecular mechanisms, context-dependent miRNA-target interactions 2014 RLSMDA [29] ✓ ✓ Â Semi-supervised prediction method, global approach 2014 miRPD [41] Â Â Â miRNAProteinDisease associations, text mining 2015 RBMMMDA [43] Â Â Â Restricted Boltzmann machine, inferring multiple types of miRNA-disease pairs 2016 WBSMDA [28] ✓ ✓ ✓ Within and between score, integrating plenty of heterogeneous biological datasets 2016 HGIMDA [58] ✓ ✓ ✓ Heterogeneous Graph Inference 2017 BRWH [48] ✓ ✓ Â bi-random walk, heterogeneous network, microbe similarity network, disease similarity network 2017 GRNMF [49] ✓ ✓ Â graph regularized NMF 2017 PBMDA [50] ✓ ✓ ✓ special depth-first search algorithm, heterogeneous graph 2017 LRSSLMDA [51] ✓ ✓ ✓ Laplacian Regularized Sparse Subspace Learning, local structures of the training data 2017 CPTL [42] ✓ ✓ Â Collective Prediction, Transduction Learning 2017 RKNNMDA [34] ✓ ✓ ✓ Ranking-based KNN 2017 MCMDA [44] Â Â Â Matrix completion, adjacency matrix of known miRNA-disease associations 2018 IMCMDA [52] ✓ ✓ ✓ Inductive Matrix Completion 2018 EGBMMDA [53] ✓ ✓ Â Extreme Gradient Boosting Machine, statistical measures, graph theoretical measures, matrix factorization results 2018 BNPMDA [54] ✓ ✓ ✓ Bipartite Network Projection, bias ratings, agglomerative hierarchical clustering 2018 TLHNMDA [55] ✓ ✓ Â Triple Layer Heterogeneous Network based inference 2018 ELLPMDA [35] ✓ ✓ ✓ Ensemble Learning, Link Prediction, similarity network 2018 MDHGI [59] ✓ ✓ ✓ Matrix decomposition and Heterogeneous Graph Inference 2018 BLHARMDA [60] ✓ ✓ ✓ Bipartite...…”

“…By constructing a similarity network and utilizing ensemble learning to combine rank results given by three classic similaritybased algorithms, Chen et al [35] obtained superior prediction results. Based on the known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, many computational models have been put forward, e.g., Sparse Subspace Learning [51], matrix completion-based model [52], Extreme Gradient Boosting Machine [53], Bipartite Network Projection [54], Triple Layer Heterogeneous Network [55] and Random Forest [56]. In Table 1, we give a brief summary of the differences between some typical previous computational methods.…”