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基于隔室特异性生物学特征的蛋白质亚细胞定位预测

Protein Subcellular Localization Prediction Based on Compartment-Specific Biological Features
课程网址: http://videolectures.net/mlss06tw_su_pslpb/  
主讲教师: Chia-Yu Su
开课单位: 信息科学研究所
开课时间: 2007-02-25
课程语种: 英语
中文简介:
预测蛋白质的亚细胞定位对于基因组注释,蛋白质功能预测和药物发现是重要的。我们提出了一种革兰氏阴性细菌的预测方法,该方法使用十个一对一支持向量机(SVM)分类器,其中选择隔室特异性生物学特征作为每个SVM分类器的输入。通过使用多数投票和概率方法的组合对来自十个二元分类器的结果进行整合来确定定位站点的最终预测。在基准数据集的十倍交叉验证评估中,总体准确度达到91.4%,比最先进的系统好1.6%。我们证明了在一对一SVM分类器中由生物学知识和见解引导的特征选择可以导致预测性能的显着改善。我们的模型还用于高度准确地预测双重定位蛋白质的92.8%总体准确度。
课程简介: Prediction of subcellular localization of proteins is important for genome annotation, protein function prediction, and drug discovery. We present a prediction method for Gram-negative bacteria that uses ten one-versus-one support vector machine (SVM) classifiers, where compartment-specific biological features are selected as input to each SVM classifier. The final prediction of localization sites is determined by inte-grating the results from ten binary classifiers using a combination of majority votes and a probabilistic method. The overall accuracy reaches 91.4%, which is 1.6% better than the state-of-the-art system, in a ten-fold cross-validation evaluation on a bench-mark data set. We demonstrate that feature selection guided by biological knowledge and insights in one-versus-one SVM classifiers can lead to a significant improvement in the prediction performance. Our model is also used to produce highly accurate prediction of 92.8% overall accuracy for proteins of dual localizations.
关 键 词: 亚细胞定位; 革兰氏阴性细菌; 蛋白质功能预测
课程来源: 视频讲座网
最后编审: 2019-08-09:cjy
阅读次数: 42