Issue No. 03 - July-September (2010 vol. 7)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.50
Fabio Rinaldi , University of Zurich
Gerold Schneider , University of Zurich, Zurich
Kaarel Kaljurand , University of Zurich, Zurich
Simon Clematide , University of Zurich, Zurich
Thérèse Vachon , Novartis Pharma AG, NITAS, Text Mining Services, Basel
Martin Romacker , Novartis Pharma AG, NITAS, Text Mining Services, Basel
We describe a system for the detection of mentions of protein-protein interactions in the biomedical scientific literature. The original system was developed as a part of the OntoGene project, which focuses on using advanced computational linguistic techniques for text mining applications in the biomedical domain. In this paper, we focus in particular on the participation to the BioCreative II.5 challenge, where the OntoGene system achieved best-ranked results. Additionally, we describe a feature-analysis experiment performed after the challenge, which shows the unexpected result that one single feature alone performs better than the combination of features used in the challenge.
Biomedical text mining, Natural Language Processing (NLP), protein interactions, BioCreative.
S. Clematide, K. Kaljurand, T. Vachon, G. Schneider, F. Rinaldi and M. Romacker, "OntoGene in BioCreative II.5," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 472-480, 2010.