A graphical model for predicting protein molecular function

Barbara Engelhardt Martin, Michael I. Jordan, Steven E. Brenner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

We present a simple statistical model of molecular function evolution to predict protein function. The model description encodes general knowledge of how molecular function evolves within a phylogenetic tree based on the proteins' sequence. Inputs axe a phylogeny for a set of evolutionaxily related protein sequences and any available function characterizations for those proteins. Posterior probabilities for each protein are used to predict the molecular function of that protein. We present results from applying our model to three protein families, and compare our prediction results on the extant proteins to other available protein function prediction methods. For the deaminase family, our method achieves 93.9% where related methods BLAST achieves 72.7%, GOtcha achieves 87.9%, and Orthostrapper achieves 72.7% in prediction accuracy.

Original languageEnglish (US)
Title of host publicationICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Pages297-304
Number of pages8
StatePublished - Oct 6 2006
EventICML 2006: 23rd International Conference on Machine Learning - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006

Publication series

NameICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Volume2006

Other

OtherICML 2006: 23rd International Conference on Machine Learning
CountryUnited States
CityPittsburgh, PA
Period6/25/066/29/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Engelhardt Martin, B., Jordan, M. I., & Brenner, S. E. (2006). A graphical model for predicting protein molecular function. In ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning (pp. 297-304). (ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning; Vol. 2006).