Paper Info
Title
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.
Abstract
The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It’s anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.
Year
DOI
Venue
2020
10.1186/s12859-020-3406-0
BMC Bioinformatics
Keywords
DocType
Volume
Sequence analysis, RNA-protein interaction, ncRNA, Ensemble learning, Position weight matrix, Legendre moments
Journal
21
Issue
ISSN
Citations 
1
1471-2105
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hai-Cheng Yi113.06
Zhuhong You274855.20
Mei-Neng Wang312.04
Zhen-Hao Guo454.52
Yan-Bin Wang544.45
Ji-Ren Zhou601.69