An empirical analysis of training protocols for probabilistic gene finders

Background: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. Results: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improveme...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: William H. Majoros, Steven Salzberg
• Keywords: BMCBI 2005, Ghmm-based Gene Finders, Global Parameter Structure, Maximum Likelihood