{"title":"部分AUC用于分化基因检测","authors":"Zhenqiu Liu, T. Hyslop","doi":"10.1109/BIBE.2010.68","DOIUrl":null,"url":null,"abstract":"Partial AUC (pAUC) represents the area with a restricted range of specificity (e.g. low false positive rate). It may identify important regional differentiated genes missed by full-range analysis. Unlike the popular t-test, which is based on the mean difference and the standard deviation between the disease and health groups, pAUC based test statistic relies on the rank of a gene in different samples. It can effectively detect genes that are not significant in a t-test and only differentiated in a subset of the disease groups. Our experiments with real gene expression data show that the proposed pAUC statistic is appealing in terms of both detection power and the biological relevance of the results.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Partial AUC for Differentiated Gene Detection\",\"authors\":\"Zhenqiu Liu, T. Hyslop\",\"doi\":\"10.1109/BIBE.2010.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Partial AUC (pAUC) represents the area with a restricted range of specificity (e.g. low false positive rate). It may identify important regional differentiated genes missed by full-range analysis. Unlike the popular t-test, which is based on the mean difference and the standard deviation between the disease and health groups, pAUC based test statistic relies on the rank of a gene in different samples. It can effectively detect genes that are not significant in a t-test and only differentiated in a subset of the disease groups. Our experiments with real gene expression data show that the proposed pAUC statistic is appealing in terms of both detection power and the biological relevance of the results.\",\"PeriodicalId\":330904,\"journal\":{\"name\":\"2010 IEEE International Conference on BioInformatics and BioEngineering\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on BioInformatics and BioEngineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2010.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on BioInformatics and BioEngineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2010.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Partial AUC (pAUC) represents the area with a restricted range of specificity (e.g. low false positive rate). It may identify important regional differentiated genes missed by full-range analysis. Unlike the popular t-test, which is based on the mean difference and the standard deviation between the disease and health groups, pAUC based test statistic relies on the rank of a gene in different samples. It can effectively detect genes that are not significant in a t-test and only differentiated in a subset of the disease groups. Our experiments with real gene expression data show that the proposed pAUC statistic is appealing in terms of both detection power and the biological relevance of the results.