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Hidden Markov models in biological sequence analysis

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1 Author(s)
Birney, E. ; European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, U.K. CB10 1SD

The vast increase of data in biology has meant that many aspects of computational science have been drawn into the field. Two areas of crucial importance are large-scale data management and machine learning. The field between computational science and biology is varyingly described as “computational biology” or “bioinformatics.” This paper reviews machine learning techniques based on the use of hidden Markov models (HMMs) for investigating biomolecular sequences. The approach is illustrated with brief descriptions of gene-prediction HMMs and protein family HMMs.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:45 ,  Issue: 3.4 )