Chapter 4 - An introduction to hidden Markov models for biological sequences

@article{Krogh1998Chapter4,
  title={Chapter 4 - An introduction to hidden Markov models for biological sequences},
  author={A. Krogh},
  journal={New Comprehensive Biochemistry},
  year={1998},
  volume={32},
  pages={45-63}
}
  • A. Krogh
  • Published 1998
  • Computer Science, Biology
  • New Comprehensive Biochemistry
This chapter discusses the hidden Markov models (HMM) for biological sequences. A hidden Markov model (HMM) is a statistical model, which is very well suited for many tasks in molecular biology, although they have been mostly developed for speech recognition. The most popular use of the HMM in molecular biology is as a probabilistic profile of a protein family, which is called a profile HMM. From a family of proteins (or DNA), a profile HMM can be made for searching a database for other members… Expand
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References

SHOWING 1-10 OF 72 REFERENCES
Hidden Markov models in computational biology. Applications to protein modeling.
TLDR
The results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling. Expand
Hidden Markov models of biological primary sequence information.
TLDR
A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family, yielding an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences. Expand
A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
TLDR
A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence and provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. Expand
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
  • A. Krogh
  • Computer Science, Medicine
  • ISMB
  • 1997
TLDR
A new (approximative) algorithm is described, which finds the most probable prediction summed over all paths yielding the same prediction, and it is shown that these methods contribute significantly to the high performance of HMMgene. Expand
Finding Genes in DNA with a Hidden Markov Model
TLDR
A new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions, called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled. Expand
A hidden Markov model that finds genes in E. coli DNA.
TLDR
The hidden Markov model finds the exact locations of about 80% of the known E. coli genes, and approximate locations for about 10%, and finds several potentially new genes and locates several places were insertion or deletion errors/and or frameshifts may be present in the contigs. Expand
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
TLDR
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced, which can improve the quality of HMMs produced from small training sets. Expand
Integrating database homology in a probabilistic gene structure model.
TLDR
An improved stochastic model of genes in DNA is presented, a method for integrating database homology into the probabilistic framework is described, and a generalized hidden Markov model (GHMM) describes the grammar of a legal parse of a DNA sequence. Expand
Characterization of Prokaryotic and Eukaryotic Promoters Using Hidden Markov Models
TLDR
It is found that HMMs trained on such subclasses of Escherichia coli promoters (specifically, the so-called sigma 70 and sigma 54 classes) give an excellent classification of unknown promoters with respect to sigma-class. Expand
Protein topology recognition from secondary structure sequences: application of the hidden Markov models to the alpha class proteins.
TLDR
The study indicates that the HMMs are useful for protein topology recognition even when no detectable primary amino acid sequence similarity is present, and will become increasingly useful as the accuracy of secondary prediction algorithms is improved. Expand
...
1
2
3
4
5
...