TY - GEN
T1 - Enzyme function classification using protein sequence features and random forest
AU - Kumar, Chetan
AU - Li, Gang
AU - Choudhary, Alok
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Enzymes are proteins that catalyze bio-chemical reactions in different ways and play important roles in metabolic pathways. The exponential rise in sequences of new enzymes has necessitated developing methods that accurately predict their function. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been applied, but are known to fail for dissimilar proteins that perform the same function. In this paper, we present a machine learning approach to accurately predict the main function class of enzymes based on a unique set of 73 sequence-derived features. Our features can be extracted using freely available online tools. We used different multi-class classifiers to categorize enzyme protein sequences into one of the NC-IUBMB defined six main function classes. Amongst the classifiers, Random Forest reported the best results with an overall accuracy of 88% and precision and recall in the range of 84% to 93% and 82% to 93% respectively. Our results compare favorably with existing methods, and in some cases report better performance. Random Forest has been proven to be a very efficient data mining algorithm. This paper is first in exploring their application to enzyme function prediction. The datasets can be accessed online at the location: http://cholera.ece.northwestern.edu/EnzyPredict.
AB - Enzymes are proteins that catalyze bio-chemical reactions in different ways and play important roles in metabolic pathways. The exponential rise in sequences of new enzymes has necessitated developing methods that accurately predict their function. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been applied, but are known to fail for dissimilar proteins that perform the same function. In this paper, we present a machine learning approach to accurately predict the main function class of enzymes based on a unique set of 73 sequence-derived features. Our features can be extracted using freely available online tools. We used different multi-class classifiers to categorize enzyme protein sequences into one of the NC-IUBMB defined six main function classes. Amongst the classifiers, Random Forest reported the best results with an overall accuracy of 88% and precision and recall in the range of 84% to 93% and 82% to 93% respectively. Our results compare favorably with existing methods, and in some cases report better performance. Random Forest has been proven to be a very efficient data mining algorithm. This paper is first in exploring their application to enzyme function prediction. The datasets can be accessed online at the location: http://cholera.ece.northwestern.edu/EnzyPredict.
KW - Enzyme function classificaiton
KW - Machine learning
KW - Multi-class random forest
KW - Protein sequence features
UR - http://www.scopus.com/inward/record.url?scp=72749086435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72749086435&partnerID=8YFLogxK
U2 - 10.1109/ICBBE.2009.5162790
DO - 10.1109/ICBBE.2009.5162790
M3 - Conference contribution
AN - SCOPUS:72749086435
SN - 9781424429028
T3 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
BT - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Y2 - 11 June 2009 through 13 June 2009
ER -