The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Hidden Markov models of biological primary sequence information.
Open accessAbstract
Hidden Markov model (HMM) techniques are used to model families of biological sequences. 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. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences.
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Cites
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Cited by
Eddy, S. R. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.9.755 · Cited by 5,515
GeneMark.hmm: new solutions for gene finding
Lukashin, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.4.1107 · Cited by 1,538
Factorial Hidden Markov Models
Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157
PROSITE: a documented database using patterns and profiles as motif descriptors
Sigrist, C. J. A. (2002) · Briefings in Bioinformatics · doi:10.1093/bib/3.3.265 · Cited by 913
The hierarchical hidden Markov model: Analysis and applications
Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906
Metadata
- Authors
- Baldi, P., Chauvin, Y., Hunkapiller, T., & McClure, M. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1994-02
- Volume
- 91
- Issue
- 3
- Pages
- 1059-1063
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2013570924
- Cited by
- 450
- References
- 1
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- green
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- Baldi, P., Chauvin, Y., Hunkapiller, T., & McClure, M. A. (1994). Hidden Markov models of biological primary sequence information. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.91.3.1059
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