ArticleBMC bioinformatics2010
Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 60 citations in OpenAlex.
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- Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV.BioMed research international · 2018Article
- Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.Oncotarget · 2017Article
- Visualizing the GPCR Network: Classification and Evolution.Scientific reports · 2017Article
- Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.PloS one · 2017Article
- Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.Molecular genetics and genomics : MGG · 2016Article
- The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.PloS one · 2016Article
- Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer.BioMed research international · 2016Article
- Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.PloS one · 2015Article
- Gene ontology and KEGG enrichment analyses of genes related to age-related macular degeneration.BioMed research international · 2014Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBecause 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.
resultsIn 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 utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred.
conclusionThe results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.
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