Learning Multimodal Networks From Heterogeneous Data for Prediction of lncRNA-miRNA Interactions.

Long noncoding RNAs (lncRNAs) is an important class of non-protein coding RNAs. They have recently been found to potentially be able to act as a regulatory molecule in some important biological processes. MicroRNAs (miRNAs) have been confirmed to be closely related to the regulation of various human diseases. Recent studies have suggested that lncRNAs could interact with miRNAs to modulate their regulatory roles. Hence, predicting lncRNA-miRNA interactions are biologically significant due to their potential roles in determining the effectiveness of diagnostic biomarkers and therapeutic targets for various human diseases. For the details of the mechanisms to be better understood, it would be useful if some computational approaches are developed to allow for such investigations. As diverse heterogeneous datasets for describing lncRNA and miRNA have been made available, it becomes more feasible for us to develop a model to describe potential interactions between lncRNAs and miRNAs. In this work, we present a novel computational approach called LMNLMI for such purpose. LMNLMI works in several phases. First, it learns patterns from expression, sequences and functional data. Based on the patterns, it then constructs several networks including an expression-similarity network, a functional-similarity network, and a sequence-similarity network. Based on a measure of similarities between these networks, LMNLMI computes an interaction score for each pair of lncRNA and miRNA in the database. The novelty of LMNLMI lies in the use of a network fusion technique to combine the patterns inherent in multiple similarity networks and a matrix completion technique in predicting interaction relationships. Using a set of real data, we show that LMNLMI can be a very effective approach for the accurate prediction of lncRNA-miRNA interactions.