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 improvement in prediction accuracy can 
be achieved by this method.
CONCLUSIONS: We conclude that training of GHMM-based gene finders is best 
performed using some form of discriminative training rather than simple maximum 
likelihood estimation at the submodel level, and that generalized gradient 
ascent methods are suitable for this task. We also conclude that partitioning of 
training data for the twin purposes of maximum likelihood initialization and 
gradient ascent optimization appears to be unnecessary, but that strict 
segregation of test data must be enforced during final gene finder evaluation to 
avoid artificially inflated accuracy measurements.