Hidden Markov Model with Duration Side Information for Novel HMMD Derivation, with Application to Eukaryotic Gene Finding. (4th October 2010)

• Record Type: Journal Article
• Authors: Winters-Hilt Winters-Hilt, S. S.; Jiang Jiang, Z. Z.; Baribault Baribault, C. C.
• Other Names: Vikalo Vikalo Haris Haris Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-10-04
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/761360
• Languages: English
• ISSNs: 1687-6172

Abstract

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.