M Craven, D Page, J Shavlik, J Bockhorst, J Glasner
{"title":"A probabilistic learning approach to whole-genome operon prediction.","authors":"M Craven, D Page, J Shavlik, J Bockhorst, J Glasner","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this task from a rich variety of data types including sequence data, gene expression data, and functional annotations associated with genes. We use multiple learned models that individually predict promoters, terminators and operons themselves. A key part of our approach is a dynamic programming method that uses our predictions to map every known and putative gene in a given genome into its most probable operon. We evaluate our approach using data from the E. coli K-12 genome.</p>","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this task from a rich variety of data types including sequence data, gene expression data, and functional annotations associated with genes. We use multiple learned models that individually predict promoters, terminators and operons themselves. A key part of our approach is a dynamic programming method that uses our predictions to map every known and putative gene in a given genome into its most probable operon. We evaluate our approach using data from the E. coli K-12 genome.