ML Anthology

A Graphical Model for Protein Secondary Structure Prediction

Wei Chu, Zoubin Ghahramani, David L. Wild
ICML 2004
doi:10.1145/1015330.1015354 /icml/2004/chu2004icml-graphical/

Abstract

In this paper, we present a graphical model for protein secondary structure prediction. This model extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles which contain information from evolutionarily related sequences. A novel parameterized model is proposed as the likelihood function for the SSMM to capture the segmental conformation. By incorporating the information from long range interactions in beta-sheets, this model is capable of carrying out inference on contact maps. The numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising.

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Cite

Text

Chu et al. "A Graphical Model for Protein Secondary Structure Prediction." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015354

Markdown

[Chu et al. "A Graphical Model for Protein Secondary Structure Prediction." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/chu2004icml-graphical/) doi:10.1145/1015330.1015354

BibTeX

@inproceedings{chu2004icml-graphical,
  title     = {{A Graphical Model for Protein Secondary Structure Prediction}},
  author    = {Chu, Wei and Ghahramani, Zoubin and Wild, David L.},
  booktitle = {International Conference on Machine Learning},
  year      = {2004},
  doi       = {10.1145/1015330.1015354},
  url       = {https://mlanthology.org/icml/2004/chu2004icml-graphical/}
}