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用离散增量结合支持向量机方法预测蛋白质亚细胞定位
引用本文:赵禹,赵巨东,姚龙.用离散增量结合支持向量机方法预测蛋白质亚细胞定位[J].生物信息学,2010,8(3):237-239,244.
作者姓名:赵禹  赵巨东  姚龙
作者单位:内蒙古工业大学理学院,呼和浩特,010051
基金项目:内蒙古工业大学重点项目ZD200418 
摘    要:对未知蛋白的功能注释是蛋白质组学的主要目标。一个关键的注释是蛋白质亚细胞定位的预测。本文应用离散增量结合支持向量机(ID_SVM)的方法,对阳性革兰氏细菌蛋白的5类亚细胞定位点进行预测。在独立检验下,其总体预测成功率为89.66%。结果发现ID_SVM算法对预测的成功率有很大改进。

关 键 词:蛋白质亚细胞定位  离散增量  支持向量机  阳性革兰氏细菌

Prediction subcellular localization of proteins using the algorithm of the increment of diversity combined with support vector machines
ZHAO Yu,ZHAO Ju-dong,YAO Long.Prediction subcellular localization of proteins using the algorithm of the increment of diversity combined with support vector machines[J].China Journal of Bioinformation,2010,8(3):237-239,244.
Authors:ZHAO Yu  ZHAO Ju-dong  YAO Long
Affiliation:(Faculty of Science,Inner Mongolia University of Technology,Hohhot 010051,China)
Abstract:Functional annotation of unknown proteins is a major goal in proteomics.A key annotation is the prediction of a protein's subcellular localization.We used the method of Increment of Diversity combined with Support Vector Machine analysis(ID_SVM) to predict subcellular localization of proteins which are recognized by the five Gram-positive bacterial sites and obtained accuracies at 89.66%,respectively in independent dataset test.So the algorithm of ID_SVM is better in predict protein subcellular location.
Keywords:Subcellular Localization  Increment of Diversity  Support Vector Machine  Gram-positive bacterial
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