SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Background: Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results: We present a new multi-class SVM-based protein fold and superfamily recognition system and web server, called SVM-Fold. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile ker...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Iain Melvin, Eugene Ie, Rui Kuang, Jason Weston, William Stafford Noble, Christina S. Leslie
• Keywords: BMCBI 2007, Protein, Protein Classification, SVM-based Protein