title: RBRIdent: An algorithm for improved identification of RNA-binding residues in proteins from primary sequences.
abstract: Rapid and correct identification of RNA-binding residues based on the protein primary sequences is of great importance. In most prevalent machine-learning-based identification methods; however, either some features are inefficiently represented, or the redundancy between features is not effectively removed. Both problems may weaken the performance of a classifier system and raise its computational complexity. Here, we addressed the above problems and developed a better classifier (RBRIdent) to identify the RNA-binding residues. In an independent benchmark test, RBRIdent achieved an accuracy of 76.79%, Matthews correlation coefficient of 0.3819 and F-measure of 75.58%, remarkably outperforming all prevalent methods. These results suggest the necessity of proper feature description and the essential role of feature selection in this project. All source data and codes are freely available at http://166.111.152.91/RBRIdent.
authors.affiliation: MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
authors.forename: Dapeng
authors.initials: D
authors.lastname: Xiong
authors.affiliation: Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
authors.forename: Jianyang
authors.initials: J
authors.lastname: Zeng
authors.affiliation: MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
authors.forename: Haipeng
authors.initials: H
authors.lastname: Gong
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D000069550:Machine Learning; D008958:Models, Molecular; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D016601:RNA-Binding Proteins
keywords: RNA-binding residues; feature selection; machine learning; sequence-based prediction