AUTHOR=Peng Lihong , Liu Fuxing , Yang Jialiang , Liu Xiaojun , Meng Yajie , Deng Xiaojun , Peng Cheng , Tian Geng , Zhou Liqian 

TITLE=Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

JOURNAL=Frontiers in Genetics

VOLUME=10

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.01346

DOI=10.3389/fgene.2019.01346

ISSN=1664-8021

ABSTRACT=<p>Identifying lncRNA–protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.</p>