 MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association predictionXing Chen, Jun Yin, Jia Qu and Li Huang PLOS Computational Biology, 2018, vol. 14, issue 8, 1-24 Abstract: Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature.Author summary: Identifying potential miRNA-disease associations enhances the understanding towards molecular mechanisms and pathogenesis of diseases, which is beneficial for the development of diagnostic/treatment tools for diseases. Compared with traditional experiment methods, computational models can help experimenters reduce the cost of money and time. In order to computationally predict potential miRNA-disease associations, we developed MDHGI by combining the sparse learning method with the heterogeneous graph inference method. We performed MDHGI on different database and the experiment results indicated that MDHGI had significant advantages over previous methods both in leave-one-out cross validation and 5-fold cross validation. Besides, we also carried out three different kinds of case studies on four important human complex diseases to further demonstrate the prediction accuracy of MDHGI. In consequence, 98%, 98%, 100% and 100% out of the top 50 candidate miRNAs for the four diseases were confirmed by different databases or experimental literatures in PubMed, respectively. Thus, it could be concluded that MDHGI could make reliable predictions and should serve as an effective tool for predicting potential miRNA-disease associations. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006418 DOI: 10.1371/journal.pcbi.1006418 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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