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Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84183

Saigo,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Saigo, H., Hattori, M., & Tsuda, K. (2007). Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism. Talk presented at NIPS 2007 Workshop on Machine Learning in Computational Biology. Whistler, BC, Canada.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CAFF-8
Abstract
Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83 accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.