BYSTRÝ, Vojtěch and Matej LEXA. cswHMM: a novel context switching hidden Markov model for biological sequence analysis. In Jan Schier, Carlos Correia, Ana Fred and Hugo Gamboa. Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. Neuveden: SciTePress, 2012. p. 208-213. ISBN 978-989-8425-90-4. doi:10.5220/0003780902080213.
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Basic information
Original name cswHMM: a novel context switching hidden Markov model for biological sequence analysis
Authors BYSTRÝ, Vojtěch (203 Czech Republic, guarantor, belonging to the institution) and Matej LEXA (703 Slovakia, belonging to the institution).
Edition Neuveden, Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. p. 208-213, 6 pp. 2012.
Publisher SciTePress
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14330/12:00059376
Organization unit Faculty of Informatics
ISBN 978-989-8425-90-4
Doi http://dx.doi.org/10.5220/0003780902080213
UT WoS 000310729400027
Keywords in English bioinformatics; data-mining; hidden Markov models
Tags International impact, Reviewed
Changed by Changed by: Ing. Matej Lexa, Ph.D., učo 31298. Changed: 23. 4. 2013 00:35.
Abstract
In this work we created a sequence model that goes beyond simple linear patterns to model a specific type of higher-order relationship possible in biological sequences. Particularly, we seek models that can account for partially overlaid and interleaved patterns in biological sequences. Our proposed context-switching model (cswHMM) is designed as a variable-order hidden Markov model (HMM) with a specific structure that allows switching control between two or more sub-models.Tests of this approach suggest that a combination of HMMs for protein sequence analysis, such as pattern mining based HMMs or profile HMMs, with the context-switching approach can improve the descriptive ability and performance of the models.
Links
LA09016, research and development projectName: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM) (Acronym: ERCIM)
Investor: Ministry of Education, Youth and Sports of the CR, Czech Republic membership in the European Research Consortium for Informatics and Mathematics
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