Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu


Kernels for structured data are rapidly becoming an essential part of the machine learning toolbox. Graph kernels provide similarity measures for complex relational objects, such as molecules and enzymes. Graph kernels based on walks are popular due their fast computation but their predictive performance is often not satisfactory, while kernels based on subgraphs suffer from high computational cost and are limited to small substructures. Kernels based on paths offer a promising middle ground between these two extremes. However, the computation of path kernels has so far been assumed computationally too challenging. In this paper we introduce an effective method for computing path based kernels; we employ a Burrows-Wheeler transform based compressed path index for fast and space-efficient enumeration of paths. Unlike many kernel algorithms the index representation retains fast access to individual features. In our experiments with chemical reaction graphs, path based kernels surpass state-of-the-art graph kernels in prediction accuracy.


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Paper Citation

in Harvard Style

Heinonen M., Välimäki N., Mäkinen V. and Rousu J. (2012). EFFICIENT PATH KERNELS FOR REACTION FUNCTION PREDICTION . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 202-207. DOI: 10.5220/0003779402020207

in Bibtex Style

author={Markus Heinonen and Niko Välimäki and Veli Mäkinen and Juho Rousu},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},

in EndNote Style

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
SN - 978-989-8425-90-4
AU - Heinonen M.
AU - Välimäki N.
AU - Mäkinen V.
AU - Rousu J.
PY - 2012
SP - 202
EP - 207
DO - 10.5220/0003779402020207