Choosing the Optimal Hidden Markov Model for Secondary-Structure Prediction November/December 2005 (vol. 20 no. 6) pp. 19-25
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIS.2005.102
Researchers have developed many methods to predict proteins' secondary structure solely on the basis of their sequences. Most of these methods rely on neural networks, which offer good accuracy but are hard to interpret. An alternative method aims to find an optimal hidden Markov model to classify protein residues into secondary-structure classes. In addition to producing models that are more easily interpreted, HMMs provide a probabilistic framework for sequence treatment. The model developed with this method features 36 hidden states and offers a compromise between prediction accuracy and a reasonable number of parameters. This article is part of a special issue on data mining for bioinformatics.
Index Terms:
protein, secondary structure prediction, hidden Markov models, HMM, model selection
Citation:
Juliette Martin, Jean-Fran?ois Gibrat, Fran?ois Rodolphe, "Choosing the Optimal Hidden Markov Model for Secondary-Structure Prediction," IEEE Intelligent Systems, vol. 20, no. 6, pp. 19-25, Nov./Dec. 2005, doi:10.1109/MIS.2005.102 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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