Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.
BACKGROUND
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.


BACKGROUND
The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Science, Systems and Models, Roskilde University, 4000 Roskilde, Denmark. soer@ruc.dk
Søren
S
Mørk
Ian
I
Holmes
D000465:Algorithms; D001185:Artificial Intelligence; D001412:Bacillus subtilis / Q000235:genetics*; D004926:Escherichia coli / Q000235:genetics*; D005798:Genes, Bacterial; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D011381:Programming Languages; D016415:Sequence Alignment