[1]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(04):431.[doi:10.14177/j.cnki.32-1397n.2016.40.04.009]
 Gao Faqi,Yu Dongjun,Shen Hongbin.Prediction of amphipathic helices in transmembrane proteins by using ensembled classifier[J].Journal of Nanjing University of Science and Technology,2016,40(04):431.[doi:10.14177/j.cnki.32-1397n.2016.40.04.009]
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基于分类器集成的跨膜蛋白两亲螺旋区域位置预测
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年04期
页码:
431
栏目:
出版日期:
2016-08-29

文章信息/Info

Title:
Prediction of amphipathic helices in transmembrane proteins by using ensembled classifier
文章编号:
1005-9830(2016)04-0431-07
作者:
郜法启13於东军2沈红斌13
1.上海交通大学 图像处理与模式识别研究所,上海 200240; 2.南京理工大学 计算机科学与工程学院,江苏 南京 210094; 3.系统控制与信息处理教育部重点实验室,上海 200240
Author(s):
Gao Faqi13Yu Dongjun2Shen Hongbin13
1.Institute of Image Processing and Pattern Recognition,Shanghai Jiao Tong University,Shanghai 200240,China; 2.School of Computer Science and Engineering,Nanjing University of Science and Technology, Nanjing 210094,China; 3.Key Laboratory of System Control and Information Processing, Ministry of Education of China,Shanghai 200240,China
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
Keywords:
transmembrane protein amphipathic helices position specific scoring matrix hydrophobic moment classifier ensemble
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2016.40.04.009
摘要:
为提高跨膜蛋白两亲螺旋区域(Amphipathic helices,AHs)预测的精度,基于蛋白质位置特异性得分矩阵、二级结构以及疏水矩,提出了一种新的衡量两亲性的螺旋周期性(Helix periodicity,HP)特征; 利用MemBrain预测器滤除跨膜区域片段并使用下采样的方法,降低了AHs的搜索空间; 在此基础上训练基于支持向量机(Support vector machine,SVM)的集成分类器用于AHs预测。为了客观评价AHs的预测性能,首次构建了领域内较为完备可用的标准数据集。在此数据集上的实验结果表明所提方法优于其他AHs预测方法。
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 imbalance.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.

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备注/Memo

备注/Memo:
收稿日期:2016-03-11 修回日期:2016-04-08
基金项目:国家自然科学基金(61373062)
作者简介:郜法启(1990-),男,硕士,主要研究方向:生物信息学、模式识别,E-mail:hdugaofaqi@163.com; 通讯作者:沈红斌(1979-),男,博士,教授,博士生导师,主要研究方向:模式识别、数据挖掘以及生物信息学,E-mail:hbshen@sjtu.edu.cn。
引文格式: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,2016,40(4):431-437.
投稿网址::http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-06-30