Evaluating bacterial gene-finding HMM structures as probabilistic logic programs

MOTIVATION: Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM,...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 28; H. 5; S. 636 - 642
Hauptverfasser: Mørk, Søren, Holmes, Ian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Oxford University Press 01.03.2012
Schlagworte:
Algorithms
Artificial Intelligence
Bacillus subtilis - genetics
bacteria
bacterial proteins
bioinformatics
Biological and medical sciences
Escherichia coli - genetics
Fundamental and applied biological sciences. Psychology
General aspects
genes
Genes, Bacterial
Markov chain
Markov Chains
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Original Papers
prediction
Programming Languages
Sequence Alignment
statistical models
Structural gene
Structure
Bacteria
ISSN:1367-4803, 1367-4811, 1367-4811
Online-Zugang:Volltext
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Zusammenfassung:MOTIVATION: Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. RESULTS: We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. AVAILABILITY: The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. CONTACT: soer@ruc.dk Supplementary information: Supplementary data are available at Bioinformatics online.
Bibliographie:ObjectType-Article-1
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Associate Editor: John Quackenbush
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btr698