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Authors: Katja Astikainen 1 ; Esa Pitkänen 1 ; Juho Rousu 1 ; Liisa Holm 1 and Sándor Szedmák 2

Affiliations: 1 University of Helsinki, Finland ; 2 University of Southampton, United Kingdom

Keyword(s): Bioinformatics, Machine learning, Kernel methods, Enzyme function prediction.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Structural Bioinformatics ; Systems Biology

Abstract: Enzyme function prediction problem is usually solved using annotation transfer methods. These methods are suitable in cases where the function of the new protein is previously characterized and included in the taxonomy such as EC hierarchy. However, given a new function that is not previously described, these approaches arguably do not offer adequate support for the human expert. In this paper, we explore a structured output learning approach, where 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. Two structured output models are learned via Kernel Density Estimation and Maximum Margin Regression to predict enzymatic reactions from sequence motifs. We bring forward two 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 experim ents demonstrate the viability of our approach. (More)

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Paper citation in several formats:
Astikainen, K.; Pitkänen, E.; Rousu, J.; Holm, L. and Szedmák, S. (2010). REACTION KERNELS - Structured Output Prediction Approaches for Novel Enzyme Function. In Proceedings of the First International Conference on Bioinformatics (BIOSTEC 2010) - BIOINFORMATICS; ISBN 978-989-674-019-1; ISSN 2184-4305, SciTePress, pages 48-55. DOI: 10.5220/0002741700480055

@conference{bioinformatics10,
author={Katja Astikainen. and Esa Pitkänen. and Juho Rousu. and Liisa Holm. and Sándor Szedmák.},
title={REACTION KERNELS - Structured Output Prediction Approaches for Novel Enzyme Function},
booktitle={Proceedings of the First International Conference on Bioinformatics (BIOSTEC 2010) - BIOINFORMATICS},
year={2010},
pages={48-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002741700480055},
isbn={978-989-674-019-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the First International Conference on Bioinformatics (BIOSTEC 2010) - BIOINFORMATICS
TI - REACTION KERNELS - Structured Output Prediction Approaches for Novel Enzyme Function
SN - 978-989-674-019-1
IS - 2184-4305
AU - Astikainen, K.
AU - Pitkänen, E.
AU - Rousu, J.
AU - Holm, L.
AU - Szedmák, S.
PY - 2010
SP - 48
EP - 55
DO - 10.5220/0002741700480055
PB - SciTePress

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