Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

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 re...

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Bibliographic Details
Published in:Frontiers in genetics 2020-01, Vol.10, p.1346-1346
Main Authors: Peng, Lihong, Liu, Fuxing, Yang, Jialiang, Liu, Xiaojun, Meng, Yajie, Deng, Xiaojun, Peng, Cheng, Tian, Geng, Zhou, Liqian
Format: Article
Language:English
Subjects:
computational method
data repositories
Genetics
lncRNA–protein interaction
machine learning-based method
network-based method
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Summary: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.
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2019.01346