cswHMM: a novel context switching hidden Markov model for biological sequence analysis
| Authors | |
|---|---|
| Year of publication | 2012 |
| Type | Article in Proceedings |
| Conference | Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. |
| MU Faculty or unit | |
| Citation | |
| 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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