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.
Craven
M
M
Dept. of Biostatistics & Medical Informatics, University of Wisconsin, Madison 53706, USA. craven@biostat.wisc.edu
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Shavlik
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Bockhorst
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Glasner
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D020869:Gene Expression Profiling / Q000379:methods; D016680:Genome, Bacterial; D008957:Models, Genetic; D008962:Models, Theoretical; D009876:Operon; D011237:Predictive Value of Tests