Struct-NB: predicting protein-RNA binding sites using structural features

Authors:
Fadi Towfic;Cornelia Caragea;David C. Gemperline;Drena Dobbs;Vasant Honavar
Affiliations:
Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011-1040, USA.;Department of Computer Science, Iowa State University, Ames, IA 50011-1040, USA.;Department of Biology, Department of Chemistry, Carthage College, 2001 Alford Park Drive, Kenosha, WI 53140-1994, USA.;Department of Genetics, Development and Cell Biology, Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011-1040, USA.;Department of Computer Science, Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011-1040, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 6
Cited 2

Measurement of protein surface shape by solid angles

Journal of Molecular Graphics
Machine Learning

Machine Learning
Machine Learning for Sequential Data: A Review

Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Learning to Decode Cognitive States from Brain Images

Machine Learning
Analysis of Protein Protein Dimeric Interfaces

BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
A weighted profile based method for Protein-RNA interacting residue prediction

Transactions on Computational Systems Biology IV

Research on classification methods of glycoside hydrolases mechanism

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Optimised design of siRNA based on multi-featured comparison and analysis of H1N1 virus

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We analyse sequence and structural features of protein-RNA interfaces using RB-147, a non-redundant dataset of protein-RNA complexes extracted from the PDB. We train classifiers using machine learning algorithms to predict protein-RNA interfaces from sequence and structure-derived features of proteins. Our experiments show that Struct-NB, a Naive Bayes classifier that exploits structural features, outperforms its counterparts that use only sequence features to predict protein-RNA binding residues.