Evaluating bacterial gene-finding HMM structures as probabilistic logic programs

Søren Mørk, Ian Holmes

    Research output: Contribution to journalJournal articleResearchpeer-review

    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.
    Translated title of the contributionEvaluering af bakterielle gen finder HMM strukturer som probabilistiske logik programmer
    Original languageEnglish
    JournalBioinformatics
    Volume28
    Issue number5
    Pages (from-to)636-642
    Number of pages7
    ISSN1367-4803
    DOIs
    Publication statusPublished - 2012

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