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论文摘要

基于SVM的革兰氏阴性菌分泌系统蛋白识别方法

A SVM based approach to identification of Gram-negative bacterial secretion system proteins

作者:余乐正(贵州师范学院化学与生命科学学院; 四川大学化学学院);赵柳青(贵州师范学院化学与生命科学学院);陈 曼(贵州师范学院化学与生命科学学院);罗杰斯(四川大学化学学院);柳凤娟(贵州师范学院化学与生命科学学院)

Author:YU Le-Zheng(School of Chemistry and Life Science, Guizhou Normal College; College of Chemistry, Sichuan University);ZHAO Liu-Qing(School of Chemistry and Life Science, Guizhou Normal College);CHEN Man(School of Chemistry and Life Science, Guizhou Normal College);LUO Jie-Si(College of Chemistry, Sichuan University);LIU Feng-Juan(School of Chemistry and Life Science, Guizhou Normal College)

收稿日期:2014-12-06          年卷(期)页码:2016,53(2):443-447

期刊名称:四川大学学报: 自然科学版

Journal Name:Journal of Sichuan University (Natural Science Edition)

关键字:革兰氏阴性细菌;分泌系统蛋白;SVM;位置特异性得分矩阵

Key words:Gram-negative bacteria; secretion system proteins; SVM; position specific scoring matrix

基金项目:贵州省科学技术基金一般项目(黔科合J字[2014]2134号);贵州师范学院校级博士项目(12BS024);贵州师范学院校级大学生科研项目(2013DXS061)

中文摘要

本文提出了一种基于SVM快速识别革兰氏阴性菌分泌系统蛋白的方法.该方法以氨基酸组成和位置特异性得分矩阵为最优特征集,充分考虑了蛋白质的序列信息及进化信息.实验结果表明,本文提出的方法对革兰氏阴性菌分泌系统蛋白具有较好的预测性能,可作为细菌分泌系统研究的有益补充.

英文摘要

A SVM based approach is proposed to rapidly identify Gram-negative bacterial secretion system proteins. With the optimization feature set consisted of amino acid composition (AAC) and position specific scoring matrix (PSSM), this method adequately takes sequence and evolution information of proteins into account. Experiments show that this method has a good performance on prediction of Gram-negative bacterial secretion system proteins, which served as a useful complement to the study of bacterial secretion system.

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