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


This publication doesn't include Faculty of Medicine. It includes Faculty of Informatics. Official publication website can be found on muni.cz.


BYSTRÝ Vojtěch LEXA Matej

Year of publication 2012
Type Article in Proceedings
Conference Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms.
MU Faculty or unit

Faculty of Informatics

Web http://www.scitepress.org/DigitalLibrary/Link.aspx?paper=79973a8a-3ae3-40b8-adc8-625c0b5645a5
Doi http://dx.doi.org/10.5220/0003780902080213
Field Informatics
Keywords bioinformatics; data-mining; hidden Markov models
Attached files
Description 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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