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

Summary 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. There...

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Bibliographic Details
Published in:Artificial intelligence in medicine 2007-02, Vol.39 (2), p.127-136
Main Authors: Rinaldi, Fabio, Schneider, Gerold, Kaljurand, Kaarel, Hess, Michael, Andronis, Christos, Konstandi, Ourania, Persidis, Andreas
Format: Article
Language:English
Subjects:
Animals
Arabidopsis thaliana
Artificial Intelligence
Automation
Biomedical literature
Databases, Factual
Dependency parsing
Humans
Information extraction
Internal Medicine
Linguistics
Mammals
Other
Periodicals as Topic
Plant Proteins
Protein interactions
Proteins - chemistry
Proteins - physiology
Publishing
Semantics
Text mining
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Description
Summary:Summary 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.
ISSN:0933-3657
1873-2860
DOI:10.1016/j.artmed.2006.08.005