A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two main functional regulation non-coding RNAs, which involves many important pathological and physiological procedures. Accumulating evidences demonstrated that the interactions between miRNAs and lncRNAs have great impact on modulations of gene expression that are related to many Human diseases. However, identification of miRNA-lncRNA interactions via bio-experimental methods suffers from high cost and time consuming. Thus, it is more and more popular for researchers to utilize computational methods in miRNA-lncRNA interactions prediction because of their high-performance. In this study, we propose a gaussian kernel similarity-based linear optimization model for predicting miRNA-lncRNA interactions. Specifically, gaussian kernel similarity method is employed to learn the miRNAs and lncRNAs similarities based on the observed heterogeneous network. Then, an integrated network is constructed by combining the observed heterogeneous network and the constructed similarities. Finally, a linear optimization model is trained to obtain the rating matrix for the unobserved links in the integrated network. To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out cross-validation (LOOCV) are implemented on the collected dataset. The experimental results show that the proposed model yields high AUCs of 0.8624, 0.9053, 0.9152 and 0.9236 in 2-fold, 5-fold, 10-fold CV and LOOCV, respectively. It is anticipated that our proposed method is promising and reliable to inferring the interactions between miRNAs and lncRNAs for further biological researches.