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This article is part of the supplement: Machine Learning for Biomedical Literature Analysis and Text Retrieval in the International Conference on Machine Learning and Applications 2011

Open Access Highly Accessed Research

Ranking relations between diseases, drugs and genes for a curation task

Simon Clematide* and Fabio Rinaldi

Author Affiliations

Institute of Computational Linguistics, University of Zurich, Binzm├╝hlestrasse 14, 8050 Zurich, Switzerland

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Journal of Biomedical Semantics 2012, 3(Suppl 3):S5  doi:10.1186/2041-1480-3-S3-S5

Published: 5 October 2012

Abstract

Background

One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Several large resources of curated relations between biomedical entities are currently available, such as the Pharmacogenomics Knowledge Base (PharmGKB) or the Comparative Toxicogenomics Database (CTD).

Biomedical text mining systems, and in particular those which deal with the extraction of relationships among entities, could make better use of the wealth of already curated material.

Results

We propose a simple and effective method based on logistic regression (also known as maximum entropy modeling) for an optimized ranking of relation candidates utilizing curated abstracts. Furthermore, we examine the effects and difficulties of using widely available metadata (i.e. MeSH terms and chemical substance index terms) for relation extraction. Cross-validation experiments result in an improvement of the ranking quality in terms of AUCiP/R by 39% (PharmGKB) and 116% (CTD) against a frequency-based baseline of 0.39 (PharmGKB) and 0.21 (CTD). For the TAP-10 metrics, we achieve an improvement of 53% (PharmGKB) and 134% (CTD) against the same baseline system (0.21 PharmGKB and 0.15 CTD).

Conclusions

Our experiments with the PharmGKB and the CTD database show a strong positive effect for the ranking of relation candidates utilizing the vast amount of curated relations covered by currently available knowledge databases. The tasks of concept identification and candidate relation generation profit from the adaptation to previously curated material. This presents an effective and practical method suitable for conservative extension and re-validation of biomedical relations from texts that has been successfully used for curation experiments with the PharmGKB and CTD database.