A probabilistic learning approach to whole-genome operon prediction.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Craven, M; Page, D; Shavlik, J; Bockhorst, J; Glasner, J

Published Date

  • 2000

Published In

Volume / Issue

  • 8 /

Start / End Page

  • 116 - 127

PubMed ID

  • 10977072

Pubmed Central ID

  • 10977072

International Standard Serial Number (ISSN)

  • 1553-0833


  • eng

Conference Location

  • United States