NDLI: Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

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Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

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Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Pan, Xiaoyong Zhu, Lin Fan, Yong-Xian Yan, Junchi
Description	Author Affiliation: Pan X ( Center for Non-Coding RNA in Technology and Health, Section for Animal Genetics, Bioinformatics and Breeding, University of Copenhagen, Denmark); Zhu L ( College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China.); Fan YX ( School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China.); Yan J ( Shanghai Jiao Tong University, China.)
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.
File Format	HTM / HTML
ISSN	14769271
Journal	Computational Biology and Chemistry
Volume Number	53PB
e-ISSN	1476928X
Language	English
Publisher	Elsevier
Publisher Date	2014-11-13
Publisher Place	Great Britain (UK)
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Medical Informatics Discipline Chemistry Discipline Computational Biology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Structural Biology Biochemistry Computational Mathematics

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