TY - GEN
T1 - A graphical model for predicting protein molecular function
AU - Engelhardt, Barbara E.
AU - Jordan, Michael I.
AU - Brenner, Steven E.
PY - 2006
Y1 - 2006
N2 - 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 are a phylogeny for a set of evolutionarily 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.
AB - 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 are a phylogeny for a set of evolutionarily 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.
UR - http://www.scopus.com/inward/record.url?scp=34250691554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34250691554&partnerID=8YFLogxK
U2 - 10.1145/1143844.1143882
DO - 10.1145/1143844.1143882
M3 - Conference contribution
AN - SCOPUS:34250691554
SN - 1595933832
SN - 9781595933836
T3 - ACM International Conference Proceeding Series
SP - 297
EP - 304
BT - ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
T2 - 23rd International Conference on Machine Learning, ICML 2006
Y2 - 25 June 2006 through 29 June 2006
ER -