Structured Output Prediction of Enzyme Function via Reaction Kernels
published: Oct. 14, 2009, recorded: September 2009, views: 3752
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Description
Enzyme function prediction is an important problem in post-genomic bioinformatics. There are two general methods for solving the problem: transfer of annotation from a similar, already annotated protein, and machine learning approaches that treat the problem as classification against a fixed taxonomy, such as Gene Ontology or the EC hierarchy. These methods are suitable in cases where the function has been previously characterized and included in the taxonomy. However, given a new function that is not previously described, existing approaches arguably do not offer adequate support for the human expert.
In this presentation, we I will present a structured output learning approach, where the enzyme function, an enzymatic reaction, is described in fine-grained fashion with so called reaction kernels which allow interpolation and extrapolation in the output (reaction) space. A structured output model is learned to predict enzymatic reactions from sequence motifs. We bring forward several choices for constructing reaction kernels and experiment with them in the remote homology case where the functions in the test set have not been seen in the training phase. Our experiments demonstrate the viability of our approach.
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Structured Output Prediction of Enzyme Function via Reaction Kernels
author: Juho Rousu, University of Helsinki
Description
Enzyme function prediction is an important problem in post-genomic bioinformatics. There are two general methods for solving the problem: transfer of annotation from a similar, already annotated protein, and machine learning approaches that treat the problem as classification against a fixed taxonomy, such as Gene Ontology or the EC hierarchy. These methods are suitable in cases where the function has been previously characterized and included in the taxonomy. However, given a new function that is not previously described, existing approaches arguably do not offer adequate support for the human expert.
In this presentation, we I will present a structured output learning approach, where the enzyme function, an enzymatic reaction, is described in fine-grained fashion with so called reaction kernels which allow interpolation and extrapolation in the output (reaction) space. A structured output model is learned to predict enzymatic reactions from sequence motifs. We bring forward several choices for constructing reaction kernels and experiment with them in the remote homology case where the functions in the test set have not been seen in the training phase. Our experiments demonstrate the viability of our approach.
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