{"title":"转录因子结合位点鉴定问题的顺序蒙特卡罗EM解决方案","authors":"Edmund S. Jackson, W. Fitzgerald","doi":"10.1109/NSSPW.2006.4378859","DOIUrl":null,"url":null,"abstract":"A significant and stubbornly intractable problem in genome sequence analysis has been the de-novo identification of transcription factor binding sites in promoter regions. Probabilistic methods have faced difficulties from prior ignorance and poor models of the biological sequence. These problems result in inference in an extremely irregular, high dimensional space. We derive and demonstrate a novel method with improved convergence to the global mode utilising an iterated particle optimisation in place of the standard Gibbs sampling approach.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Sequential Monte Carlo EM Solution to the Transcription Factor Binding Site Identification Problem\",\"authors\":\"Edmund S. Jackson, W. Fitzgerald\",\"doi\":\"10.1109/NSSPW.2006.4378859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A significant and stubbornly intractable problem in genome sequence analysis has been the de-novo identification of transcription factor binding sites in promoter regions. Probabilistic methods have faced difficulties from prior ignorance and poor models of the biological sequence. These problems result in inference in an extremely irregular, high dimensional space. We derive and demonstrate a novel method with improved convergence to the global mode utilising an iterated particle optimisation in place of the standard Gibbs sampling approach.\",\"PeriodicalId\":388611,\"journal\":{\"name\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSPW.2006.4378859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSPW.2006.4378859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Sequential Monte Carlo EM Solution to the Transcription Factor Binding Site Identification Problem
A significant and stubbornly intractable problem in genome sequence analysis has been the de-novo identification of transcription factor binding sites in promoter regions. Probabilistic methods have faced difficulties from prior ignorance and poor models of the biological sequence. These problems result in inference in an extremely irregular, high dimensional space. We derive and demonstrate a novel method with improved convergence to the global mode utilising an iterated particle optimisation in place of the standard Gibbs sampling approach.