RBRIdent: An algorithm for improved identification of RNA‐binding residues in proteins from primary sequences. Issue 6 (22nd April 2015)

• Record Type: Journal Article
• Authors: Xiong, Dapeng; Zeng, Jianyang; Gong, Haipeng
• Is Part Of: Proteins. Volume 83:Issue 6(2015)
• Page Start: 1068
• Page End: 1077
• Publication Date: 2015-04-22
• Subjects: Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.24806
• Languages: English
• ISSNs: 0887-3585

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>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 <ext-link ext-link-type="uri" xlink:href="http://166.111.152.91/RBRIdent" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">http://166.111.152.91/RBRIdent</ext-link>. Proteins 2015; 83:1068–1077. © 2015 Wiley Periodicals, Inc.</p> </abstract>