Predicting Golgi-resident proteins in plants by incorporating N-terminal transmembrane domain information in the general form of Chou's pseudoamino acid compositions
{"title":"Predicting Golgi-resident proteins in plants by incorporating N-terminal transmembrane domain information in the general form of Chou's pseudoamino acid compositions","authors":"Yasen Jiao, Pufeng Du, Xiaoquan Su","doi":"10.1109/ISB.2014.6990759","DOIUrl":null,"url":null,"abstract":"Knowing the subcellular location of a protein is an important step in understanding its biological functions. In this paper, we developed a new method to identify whether a protein is a Golgi-resident protein or not in plant cells. We proposed to incorporate transmembrane domain information and six different kinds of physicochemical properties of amino acids in the general form of Chou's pseudo-amino acid compositions. By using SVM based classifiers, our method achieved over 90% prediction accuracy in a 5-fold cross validation, which is much better than the other state-of-the-art methods.","PeriodicalId":249103,"journal":{"name":"2014 8th International Conference on Systems Biology (ISB)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 8th International Conference on Systems Biology (ISB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISB.2014.6990759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Knowing the subcellular location of a protein is an important step in understanding its biological functions. In this paper, we developed a new method to identify whether a protein is a Golgi-resident protein or not in plant cells. We proposed to incorporate transmembrane domain information and six different kinds of physicochemical properties of amino acids in the general form of Chou's pseudo-amino acid compositions. By using SVM based classifiers, our method achieved over 90% prediction accuracy in a 5-fold cross validation, which is much better than the other state-of-the-art methods.