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Advances in Bioinformatics
Volume 2012 (2012), Article ID 582765, 12 pages
Research Article

Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations

1Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium
2Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium
3Department of Information Technology, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland
4Turku BioNLP Group, Turku Centre for Computer Science (TUCS), Joukahaisenkatu 3-5, 20520 Turku, Finland

Received 22 November 2011; Revised 16 March 2012; Accepted 28 March 2012

Academic Editor: Jin-Dong Kim

Copyright © 2012 Sofie Van Landeghem et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Technological advancements in the field of genetics have led not only to an abundance of experimental data, but also caused an exponential increase of the number of published biomolecular studies. Text mining is widely accepted as a promising technique to help researchers in the life sciences deal with the amount of available literature. This paper presents a freely available web application built on top of 21.3 million detailed biomolecular events extracted from all PubMed abstracts. These text mining results were generated by a state-of-the-art event extraction system and enriched with gene family associations and abstract generalizations, accounting for lexical variants and synonymy. The EVEX resource locates relevant literature on phosphorylation, regulation targets, binding partners, and several other biomolecular events and assigns confidence values to these events. The search function accepts official gene/protein symbols as well as common names from all species. Finally, the web application is a powerful tool for generating homology-based hypotheses as well as novel, indirect associations between genes and proteins such as coregulators.