Publication details

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


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

Field Informatics
Keywords bioinformatics; data-mining; hidden Markov models
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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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