LOMDA: Linear optimization for miRNA-disease association prediction

Abstract

MicroRNAs (miRNAs) have been playing a crucial role in many important biological processes e.g., pathogenesis of diseases. Currently, the validated associations between miRNAs and diseases are insufficient comparing to the hidden associations. Testing all these hidden associations by biological experiments is expensive, laborious, and time consuming. Therefore, computationally inferring hidden associations from biological datasets for further laboratory experiments has attracted increasing interests from different communities ranging from biological to computational science. In this work, we propose an effective and efficient method to predict associations between miRNAs and diseases, namely linear optimization (LOMDA). The proposed method uses the heterogenous matrix incorporating of miRNA functional similarity information, disease similarity information and known miRNA-disease associations. Compared with the other methods, LOMDA performs best in terms of AUC (0.970), precision (0.566), and accuracy (0.971) in average over 15 diseases in local 5-fold cross-validation. Moreover, LOMDA has also been applied to two types of case studies. In the first case study, 30 predictions from breast neoplasms, 24 from colon neoplasms, and 26 from kidney neoplasms among top 30 predicted miRNAs are confirmed. In the second case study, for new diseases without any known associations, top 30 predictions from hepatocellular carcinoma and 29 from lung neoplasms among top 30 predicted miRNAs are confirmed. Author summaryIdentifying associations between miRNAs and diseases is significant in investigation of pathogenesis, diagnosis, treatment and preventions of related diseases. Employing computational methods to predict the hidden associations based on known associations and focus on those predicted associations can sharply reduce the experimental costs. We developed a computational method LOMDA based on the linear optimization technique to predict the hidden associations. In addition to the observed associations, LOMDA also can employ the auxiliary information (diseases and miRNAs similarity information) flexibly and effectively. Numerical experiments on global 5-fold cross validation show that the use of the auxiliary information can greatly improve the prediction performance. Meanwhile, the result on local 5-fold cross validation shows that LOMDA performs best among the seven related methods. We further test the prediction performance of LOMDA for two types of diseases based on HDMMv2.0 (2014), including (i) diseases with all the known associations, and (ii) new diseases without known associations. Three independent or updated databases (dbDEMC, 2010; miR2Disease, 2009; HDMMv3.2, 2019) are introduced to evaluate the prediction results. As a result, most miRNAs for target diseases are confirmed by at least one of the three databases. So, we believe that LOMDA can guide experiments to identify the hidden miRNA-disease associations.