title: Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.
abstract: Protein-RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein-RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis.
authors.affiliation: Center for Non-Coding RNA in Technology and Health, Section for Animal Genetics, Bioinformatics and Breeding, University of Copenhagen, Denmark; Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark. Electronic address: xypan172436@gmail.com.
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Pan
authors.affiliation: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhu
authors.affiliation: School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China.
authors.forename: Yong-Xian
authors.initials: YX
authors.lastname: Fan
authors.affiliation: Shanghai Jiao Tong University, China.
authors.forename: Junchi
authors.initials: J
authors.lastname: Yan
keywords: Evolution information; Protein–RNA interaction site; Random forest; Sample imbalance; Submodularity subset selection