cswHMM: a novel context switching hidden Markov model for biological sequence analysis

Autoři BYSTRÝ Vojtěch LEXA Matej
Druh Článek ve sborníku
Citace BYSTRÝ, Vojtěch a 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. s. 208-213, 6 s. ISBN 978-989-8425-90-4. doi:10.5220/0003780902080213.
Originální jazyk angličtina
Obor Informatika
WWW http://www.scitepress.org/DigitalLibrary/Link.aspx?paper=79973a8a-3ae3-40b8-adc8-625c0b5645a5
Doi http://dx.doi.org/10.5220/0003780902080213
Klíčová slova bioinformatics; data-mining; hidden Markov models

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.

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