简介:AcomputationalsystemforthepredictionandclassificationofhumanG-proteincoupledreceptors(GPCRs)hasbeendevelopedbasedonthesupportvectormachine(SVM)methodandproteinsequenceinformation.ThefeaturevectorsusedtodeveloptheSVMpredictionmodelsconsistofstatisticallysignificantfeaturesselectedfromsingleaminoacid,dipeptide,andtripeptidecompositionsofproteinsequences.Furthermore,thelengthdistributiondifferencebetweenGPCRsandnon-GPCRshasalsobeenexploitedtoimprovethepredictionperformance.ThetestingresultswithannotatedhumanproteinsequencesdemonstratethatthissystemcangetgoodperformanceforbothpredictionandclassificationofhumanGPCRs.