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Evaluating bacterial gene-finding HMM structures as probabilistic logic programs
- Source :
- Bioinformatics. 28:636-642
- Publication Year :
- 2012
- Publisher :
- Oxford University Press (OUP), 2012.
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
Source code
Computer science
media_common.quotation_subject
computer.software_genre
Machine learning
Biochemistry
Prolog
Artificial Intelligence
Escherichia coli
Hidden Markov model
Molecular Biology
media_common
computer.programming_language
Structure (mathematical logic)
Models, Statistical
Models, Genetic
Markov chain
business.industry
Probabilistic logic
Statistical model
Original Papers
Markov Chains
Computer Science Applications
Computational Mathematics
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Genes, Bacterial
Programming Languages
Data mining
Artificial intelligence
business
Sequence Alignment
computer
Algorithms
Bacillus subtilis
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....d0848064c5567ea3f2115109fcb2dd93