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

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 s

SUBMITTER: Mork S 

PROVIDER: S-EPMC3289911 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature