Abstract
lncRNA–protein interactions play essential roles in a variety of cellular processes. However, the experimental methods for systematically mapping of lncRNA–protein interactions remain time-consuming and expensive. Therefore, it is urgent to develop reliable computational methods for predicting lncRNA–protein interactions. In this study, we propose a computational method called LncPNet to predict potential lncRNA–protein interactions by embedding an lncRNA–protein heterogenous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from the NPInter database with an accuracy of 0.930 and area under ROC curve (AUC) of 0.971. In addition, we further compare our method with other eight state-of-the-art methods, and the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA–protein heterogenous network, which will greatly benefit the prediction of lncRNA–protein interactions.
Highlights
The non-coding RNA plays important roles in biological processes, which can influence human health on various levels (Louro et al, 2009)
We propose a new long non-coding RNAs (lncRNAs)–protein interactions prediction model called LncPNet based on heterogenous network embedding, which can solve the aforementioned problems in the existing methods
support vector machines (SVMs) achieves the area under ROC curve (AUC) of 0.971 on the NPInter v2.0 dataset. It increases by 4.7% over naive Bayesian (NB) with the AUC of 0.924 and decreases by 0.1% over random forest (RF) with the AUC of 0.972
Summary
The non-coding RNA (ncRNA) plays important roles in biological processes, which can influence human health on various levels (Louro et al, 2009). The basis for understanding the functions of lncRNAs is to recognize the interactions between lncRNAs and proteins, which can help understand the mechanism of physiological processes. Experimental methods for identifying protein–RNA interactions include ChiRP, CHART, RIP, RIP-ChIP/Seq, and CLIP (Yang et al, 2015). Since these experimental methods are often time-consuming and expensive, an effective computational method is an alternative way for expanding our knowledge of lncRNA–protein interactions (Liu, 2021)
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