Combining segmental semi-Markov models with neural networks for protein secondary structure prediction

@article{Bidargaddi2009CombiningSS,
  title={Combining segmental semi-Markov models with neural networks for protein secondary structure prediction},
  author={Niranjan P. Bidargaddi and Madhu Chetty and Joarder Kamruzzaman},
  journal={Neurocomputing},
  year={2009},
  volume={72},
  pages={3943-3950}
}
Motivation: Predicting the secondary structure of proteins from a primary sequence alone has been variously approached from either a classification or a generative model perspective. The most prominent classification methods have used neural networks, which involves mappings from a local window of residues in the sequence to the structural state of the central residue in the window, thus residues. The generative models based on Bayesian segmentation capture sequence structure relationships… CONTINUE READING
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