{"title":"基于复合向量的支持向量机预测膜蛋白类型","authors":"Ting Wang, Xiuzhen Hu","doi":"10.1109/BMEI.2009.5305127","DOIUrl":null,"url":null,"abstract":"By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the eight types of membrane proteins is proposed. The overall jackknife success rate is 91.81% what is higher than other results. In order to evaluate the predictive method, the six types of membrane proteins are predicted by using our method. The better results are obtained.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"193 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Membrane Protein Types by Using Support Vector Machine Based on Composite Vector\",\"authors\":\"Ting Wang, Xiuzhen Hu\",\"doi\":\"10.1109/BMEI.2009.5305127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the eight types of membrane proteins is proposed. The overall jackknife success rate is 91.81% what is higher than other results. In order to evaluate the predictive method, the six types of membrane proteins are predicted by using our method. The better results are obtained.\",\"PeriodicalId\":6389,\"journal\":{\"name\":\"2009 2nd International Conference on Biomedical Engineering and Informatics\",\"volume\":\"193 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 2nd International Conference on Biomedical Engineering and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEI.2009.5305127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 2nd International Conference on Biomedical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEI.2009.5305127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Membrane Protein Types by Using Support Vector Machine Based on Composite Vector
By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the eight types of membrane proteins is proposed. The overall jackknife success rate is 91.81% what is higher than other results. In order to evaluate the predictive method, the six types of membrane proteins are predicted by using our method. The better results are obtained.
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