PMID:10977072
| Citation |
Craven, M, Page, D, Shavlik, J, Bockhorst, J and Glasner, J (2000) A probabilistic learning approach to whole-genome operon prediction. Proc Int Conf Intell Syst Mol Biol 8:116-27 |
|---|---|
| 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. |
| Links | |
| Keywords |
Gene Expression Profiling/methods; Genome, Bacterial; Models, Genetic; Models, Theoretical; Operon; Predictive Value of Tests |
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