This article is part of the supplement: Selected Proceedings of Machine Learning in Systems Biology: MLSB 2007 .

Proceedings

Towards structured output prediction of enzyme function

Katja Astikainen¹ , Liisa Holm² , Esa Pitkänen¹ , Sandor Szedmak³ and Juho Rousu¹

¹  Department of Computer Science, PO Box 68, FI-00014 University of Helsinki, Finland

²  Institute of Biotechnology, P.O. Box 56, FI-00014 University of Helsinki, Finland

³  Electronics and Computer Science, University of Southampton, SO17 1BJ, UK

author email corresponding author email

BMC Proceedings 2008, 2(Suppl 4):S2

Published:	17 December 2008

Abstract

Background

In this paper we describe work in progress in developing kernel methods for enzyme function prediction. Our focus is in developing so called structured output prediction methods, where the enzymatic reaction is the combinatorial target object for prediction. We compared two structured output prediction methods, the Hierarchical Max-Margin Markov algorithm (HM³) and the Maximum Margin Regression algorithm (MMR) in hierarchical classification of enzyme function. As sequence features we use various string kernels and the GTG feature set derived from the global alignment trace graph of protein sequences.

Results

In our experiments, in predicting enzyme EC classification we obtain over 85% accuracy (predicting the four digit EC code) and over 91% microlabel F1 score (predicting individual EC digits). In predicting the Gold Standard enzyme families, we obtain over 79% accuracy (predicting family correctly) and over 89% microlabel F1 score (predicting superfamilies and families). In the latter case, structured output methods are significantly more accurate than nearest neighbor classifier. A polynomial kernel over the GTG feature set turned out to be a prerequisite for accurate function prediction. Combining GTG with string kernels boosted accuracy slightly in the case of EC class prediction.

Conclusion

Structured output prediction with GTG features is shown to be computationally feasible and to have accuracy on par with state-of-the-art approaches in enzyme function prediction.