Hidden Markov Model with Duration Side Information for Novel HMMD Derivation, with Application to Eukaryotic Gene Finding

作者:Winters Hilt S*; Jiang Z; Baribault C
来源:Eurasip Journal on Advances in Signal Processing, 2010, 761360.
DOI:10.1155/2010/761360

摘要

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.

  • 出版日期2010