Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene

Background: Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs. Results: In this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utili...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Zhanchao Li, Xuan Zhou, Zong Dai, Xiaoyong Zou
• Keywords: BMCBI 2010, Optimized Feature Subsets, Relevance Minimum Redundancy, Three-layer Predictor