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

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Original title:cswHMM: a novel context switching hidden Markov model for biological sequence analysis
Authors:Vojtěch Bystrý, Matej Lexa
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Original language:English
Field:Informatics
WWW:link to a new windowhttp://www.scitepress.org/DigitalLibrary/Link.aspx?paper=79973a8a-3ae3-40b8-adc8-625c0b5645a5
Type:Article in Proceedings
Keywords:bioinformatics; data-mining; hidden Markov models
Attached files:link to a new window37809.pdf

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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