AUTHOR=Zhou Yuan-Ke , Shen Zi-Ang , Yu Han , Luo Tao , Gao Yang , Du Pu-Feng TITLE=Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model JOURNAL=Frontiers in Genetics VOLUME=10 YEAR=2020 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.01341 DOI=10.3389/fgene.2019.01341 ISSN=1664-8021 ABSTRACT=

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA–protein interactions are usually involved. Therefore, identifying lncRNA–protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA–protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA–protein interaction predictors usually require prior knowledge of lncRNA–protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA–protein interactions. In this paper, we explored a novel way to predict lncRNA–protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA–protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA–protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).