Brief communication: Hidden Markov models and optimized sequence alignments

Authors:
L. Smith;L. Yeganova;W. J. Wilbur
Affiliations:
Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, Rm. 614D, Bldg. 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA;Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, Rm. 614D, Bldg. 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA;Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, Rm. 614D, Bldg. 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
Venue:
Computational Biology and Chemistry
Year:
2003

Citing 1
Cited 2

Contrast and variability in gene names

BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3

Using local alignments for relation recognition

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Identification of related gene/protein names based on an HMM of name variations

Computational Biology and Chemistry

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Abstract

We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process. The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.