A Probabilistic Learning Approach to Whole-Genome Operon Prediction

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.

• Type: Conference
• Year: 2000
• Where: ISMB
• Authors: Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner
• Keywords: Approach Uses Machine, Computational Biology, Gene Expression Data, ISMB 2000, Operons