LRMCMDA: Predicting miRNA-disease association by integrating low-rank matrix completion with miRNA and disease similarity information

Junlin Xu, Wen Zhu, Lijun Cai, Bo Liao, Yajie Meng, Ju Xiang, Dawei Yuan, Geng Tian, Jialiang Yang
2020 IEEE Access  
Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking "negative samples" (i.e. confirmed unrelated miRNA-disease pairs). In this study, we proposed LRMCMDA, a low-rank matrix completion-based method to predict miRNA-disease associations. LRMCMDA firstly constructs a bipartite miRNA-disease graph from known associations and defines its R-projected
more » ... nes its R-projected miRNA graph, in which two miRNAs are connected if they are adjacent to the same disease in the bipartite graph. Similarly, we can define its D-projected disease graph. It then infers negative samples by assuming that connecting an unrelated miRNA-disease pair in the bipartite graph will change its R-projected miRNA graph and D-projected disease graph. Providing with both known miRNA-disease associations and negative samples, LRMCMDA infers associations between all miRNAs and diseases using a low-rank matrix completion model, in which miRNA similarity and disease similarity are incorporated into regularization terms. The assumption is that similar miRNAs will associate with similar diseases and vice versa. We compared LRMCMDA with a few state-of-the-art algorithms on several established miRNA-disease databases. LRMCMDA achieves an AUC of 0.8882 on the 5-fold cross-validation, significantly outperforming canonical methods when predicting miRNA-disease associations, and associating miRNAs with isolated diseases. The experimental results demonstrate that LRMCMDA effectively infers novel miRNA-disease associations. In addition, the case studies on cancers have further proven that LRMCMDA is useful in identifying potential cancer-associated miRNAs for experimental validation. INDEX TERMS miRNA-disease association, low-rank matrix completion, receiver operating characteristic curve (ROC), area under curve (AUC), area under the precise recall curve (AUPR). Genomics Institute of BGI, Research and Development Department, Shenzhen. He worked as the Director of the Genomics and Synthetic Biology Center, Tsinghua University. He is currently the Co-Founder and the CEO of Genesis (Beijing) Company Ltd. He has participated in several 863 and 973 projects. He has published more than ten articles with Nature and so on. He holds over ten invention patents. JIALIANG YANG received the Ph.D. degree from the
doi:10.1109/access.2020.2990533 fatcat:qajej5akbffjjhgidqgrguwq2e