Prediction of Eukaryotic Protein Subcellular Location Using a Novel Feature Extraction Method and Support Vector Machine  

作　　者：Zhang Shaowu Pan Quan Wu Yonghong Cheng Yongmei 

机构地区：[1]School of Life Sciences, Northwestern Polytechnic University, Xi＇an 710072, China [2]School of Automatic Control, Northwestern Polytechnic University, Xi＇an 710072, China

出　　处：《西北工业大学学报》2005年第6期798-803,共6页Journal of Northwestern Polytechnical University

摘　　要：The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understanding its function.The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm.The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction.To predict the subcellular location of eukaryotic protein,a systematic prediction approach comprised of a novel feature extraction method,an idea of combining this feature extraction method with support vector machine(SVM) algorithm,and ’one-versus-rest’ & ’all-versus-all’ strategies have been proposed in this paper.Consequently,the total predictive accuracies reach 95.5% for four locations.Compared with existing methods,this new approach provides better predictive performance.For example,it is 13.5%,5.1% higher than Yuan’s and Hua’s methods respectively.These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location.It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features.The rapidly increasing number of sequences created the need for fully automated methods to analyze entering into the g them. Knowing the enome databank has cellular location of a protein is a key step towards understanding its function. The development in statistical predic of protein attributes generally consists of two cores： one is to construct a training dataset and other is to formulate a predictive algorithm. The latter can be further separated into subcores： one is how to give a mathematical expression to effectively represent a protein and other is how to find a powerful algorithm to accurately perform the prediction. To predict subcellular location of eukaryotic protein, a systematic prediction approach comprised of a n tion the two the the ovel feature extraction method, an idea of combining this feature extraction method with support vector machine （SVM） algorithm, and ‘one-versus-rest＇ ‘all-versus-all＇ strategies have been proposed in this paper. Consequently, the total predictive accuracies reach 95. 5% for four locations. Compared with existing methods, this new approach provides better predictive performance. For example, it is 13. 5%, 5. 1% higher than Yuan＇s and Hua＇s methods respectively. These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location. It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features

关 键 词：亚细胞蛋白质 真核状态 异常特征抽取 亚细胞位置 媒介支持仪器 

分 类 号：Q617[生物学—生物物理学]