Hidden Markov model with duration side information for novel HMMD derivation, with application to eukaryotic gene finding

Authors:
S. Winters-Hilt;Z. Jiang;C. Baribault
Affiliations:
Department of Computer Science, University of New Orleans, New Orleans, LA and Research Institute for Children, Children's Hospital, New Orleans, LA;Department of Computer Science, University of New Orleans, New Orleans, LA;Department of Computer Science, University of New Orleans, New Orleans, LA
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Year:
2010

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Abstract

We describe a new method to introduce duration into an HMM using side information that can be put in the form of a martingale series. Our method makes use of ratios of duration cumulant probabilities in a manner that meshes with the column-level dynamic programming construction. Other information that could be incorporated, via ratios of sequence matches, includes an EST and homology information. A familiar occurrence of a martingale in HMM-based efforts is the sequence-likelihood ratio classification. Our method suggests a general procedure for piggybacking other side information as ratios of side information probabilities, in association (e.g., one-to-one) with the duration-probability ratios. Using our method, the HMM can be fully informed by the side information available during its dynamic table optimization--in Viterbi path calculations in particular.