{"title":"利用二核苷酸特征级联多级启动子识别大肠杆菌","authors":"T. Rani, R. Bapi","doi":"10.1109/ICIT.2008.56","DOIUrl":null,"url":null,"abstract":"Promoter recognition has been attempted using different paradigms such as motif/binding regions alone or whole promoter itself. In an earlier paper, a scheme is proposed to use 2-gram features to represent a promoter. These 2-grams gave a comparable performance with the existing methods in the literature. An in-depth analysis of data sets using 2-grams is performed. The analysis presented a scenario where there is a confusion between a majority of promoters with a minor set of non-promoter and vice versa. In an effort to build a complete classification system, using the majority and minority sets in promoters as well as non-promoters, a multi-level cascading system and Ada-Boost classifier are applied. The results indicate that much further improvement is not possible with the modifications proposed.","PeriodicalId":184201,"journal":{"name":"2008 International Conference on Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Cascaded Multi-level Promoter Recognition of E. coli Using Dinucleotide Features\",\"authors\":\"T. Rani, R. Bapi\",\"doi\":\"10.1109/ICIT.2008.56\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Promoter recognition has been attempted using different paradigms such as motif/binding regions alone or whole promoter itself. In an earlier paper, a scheme is proposed to use 2-gram features to represent a promoter. These 2-grams gave a comparable performance with the existing methods in the literature. An in-depth analysis of data sets using 2-grams is performed. The analysis presented a scenario where there is a confusion between a majority of promoters with a minor set of non-promoter and vice versa. In an effort to build a complete classification system, using the majority and minority sets in promoters as well as non-promoters, a multi-level cascading system and Ada-Boost classifier are applied. The results indicate that much further improvement is not possible with the modifications proposed.\",\"PeriodicalId\":184201,\"journal\":{\"name\":\"2008 International Conference on Information Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2008.56\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2008.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cascaded Multi-level Promoter Recognition of E. coli Using Dinucleotide Features
Promoter recognition has been attempted using different paradigms such as motif/binding regions alone or whole promoter itself. In an earlier paper, a scheme is proposed to use 2-gram features to represent a promoter. These 2-grams gave a comparable performance with the existing methods in the literature. An in-depth analysis of data sets using 2-grams is performed. The analysis presented a scenario where there is a confusion between a majority of promoters with a minor set of non-promoter and vice versa. In an effort to build a complete classification system, using the majority and minority sets in promoters as well as non-promoters, a multi-level cascading system and Ada-Boost classifier are applied. The results indicate that much further improvement is not possible with the modifications proposed.