Maximum Discrimination Hidden Markov Models of Sequence Consensus

@article{Eddy1995MaximumDH,
  title={Maximum Discrimination Hidden Markov Models of Sequence Consensus},
  author={Sean R. Eddy and Graeme J. Mitchison and Richard Durbin},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={1995},
  volume={2 1},
  pages={9-23}
}
We introduce a maximum discrimination method for building hidden Markov models (HMMs) of protein or nucleic acid primary sequence consensus. The method compensates for biased representation in sequence data sets, superseding the need for sequence weighting methods. Maximum discrimination HMMs are more sensitive for detecting distant sequence homologs than various other HMM methods or BLAST when tested on globin and protein kinase catalytic domain sequences. 
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