南京理工大学学报（自然科学版）2016，Vol.40Issue(4)：431-437,7.DOI:10.14177/j.cnki.32-1397n.2016.40.04.009
基于分类器集成的跨膜蛋白两亲螺旋区域位置预测
Prediction of amphipathic helices in transmembrane proteins by using ensembled classifier
摘要
Abstract
In order to improve the prediction accuracy of amphipathic helices ( AHs ) , this paper develops a novel helix periodicity( HP) feature based on the position specific scoring matrix( PSSM) , protein secondary structure and hydrophobic moment. MemBrain predictor is utilized to cut off the transmembrane segments;under-sampling and classifier ensemble are applied to cope with class im-balance. This paper implementes an ensembled support vector machine ( SVM ) classifier for performing AHs prediction. To objectively evaluate the prediction performance of AHs, a relative large benchmark data set regarding AHs prediction is constructed. Rigorous experimental tests demonstrate that the proposed method outperforms the existing AHs predictors on benchmark dataset.关键词
跨膜蛋白/两亲螺旋区域/位置特异性得分矩阵/疏水矩/分类器集成Key words
transmembrane protein/amphipathic helices/position specific scoring matrix/hydrophobic moment/classifier ensemble分类
信息技术与安全科学引用本文复制引用
郜法启,於东军,沈红斌..基于分类器集成的跨膜蛋白两亲螺旋区域位置预测[J].南京理工大学学报（自然科学版）,2016,40(4):431-437,7.基金项目
国家自然科学基金(61373062) （61373062）
