This article is part of the supplement: Workshop on Advances in Bio Text Mining

Poster presentation

A fast and effective dependency graph kernel for PPI relation extraction

Domonkos Tikk^1,2*, Peter Palaga¹ and Ulf Leser¹

• * Corresponding author: Domonkos Tikk tikk@informatik.hu-berlin.de

Author Affiliations

¹ Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

² Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar Tudósok krt 2., Hungary

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BMC Bioinformatics 2010, 11(Suppl 5):P8 doi:10.1186/1471-2105-11-S5-P8

Published: 6 October 2010

First paragraph (this article has no abstract)

Extraction of protein-protein interactions (PPIs) reported in scientific publications is a core topic of biomedical text mining. The ultimate goal is to devise a PPI extraction method that performs well on large amount of unseen text independently from the training corpus. One popular, machine-learning based approach to PPI extraction builds on the convolution kernels, i.e., similarity functions defined on the parse-based representation of sentences and interactions. Kernel functions differ in (1) the underlying sentence representation (bag-of-words, syntax tree parse, dependency graphs), (2) the substructures retrieved from the sentence representation to define interactions, and (3) calculation of the similarity function.