Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach

  • Authors:

  • Fabio Rinaldi;Gerold Schneider;Kaarel Kaljurand;Michael Hess;Christos Andronis;Ourania Konstandi;Andreas Persidis

  • Affiliations:

  • Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, CH-8050 Zürich, Switzerland;Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, CH-8050 Zürich, Switzerland;Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, CH-8050 Zürich, Switzerland;Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, CH-8050 Zürich, Switzerland;Biovista, 34 Rodopoleos Str., Ellinikon, GR-16777 Athens, Greece;Biovista, 34 Rodopoleos Str., Ellinikon, GR-16777 Athens, Greece;Biovista, 34 Rodopoleos Str., Ellinikon, GR-16777 Athens, Greece

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

Objective: The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. Materials and methods: This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. Results: We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. Conclusion: We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.