Hidden Markov models

Current Opinion in Structural Biology

Volumn 6, Issue 3, 1996, Pages 361-365

  Eddy, Sean R ^a  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC PROCEDURES; GENOME; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROTEIN STRUCTURE; SHORT SURVEY; STATISTICAL MODEL;

EID: 0029937428     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(96)80056-X     Document Type: Article

References (33)

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18. Allison L, Wallace CS: The posterior probability distribution of alignments and its application to parameter estimation of evolutionary trees and to optimization of multiple alignments. J Mol Evol 1994, 39:418-430. The authors' work is HMM-ish in character, but written in the language of information theory (minimum message length) rather than probabilistic modeling (maximum likelihood). The contrast is instructive.
19. Shortle D: Protein fold recognition. Nat Struct Biol 1995, 2:91-93. This is a short review of the Asilomar conference, at which current methods for protein structure prediction were rigorously compared. Be aware, though, that Shortle confuses threading methods, which deal with pairwise residue correlations in protein structure, with HMMs, which don't.
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24. Krogh A, Mian IS, Haussler D: A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Res 1994, 22:4768-4778. This is a nice illustration of the power of HMM methods to integrate various kinds of information into a single probabilistic model. Krogh's gene model includes a statistical description of ribosome binding sites, start and stop codons, codon usage, and intergenic repetitive elements.
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