Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 43, Issue 2, Pages 200-207Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2009.09.007
Keywords
Text mining; Information extraction; Protein-protein interaction; Semantic relations
Funding
- EC STREP [FP6-028099]
- NoE [507505]
Ask authors/readers for more resources
Motivation: The identification of events such as protein-protein interactions (PPIs) from the scientific literature is a complex task. One of the reasons is that there is no formal syntax to denote such relations in the scientific literature. Nonetheless, it is important to understand such relational event representations to improve information extraction solutions (e.g., for gene regulatory events). In this study, we analyze publicly available protein interaction corpora (AIMed, BioInfer, BioCreAtIve II) to determine the scope of verbs used to denote protein interactions and to measure their predictive capacity for the identification of PPI events. Our analysis is based on syntactical language patterns. This restriction has the advantage that the verb mention is used as the independent variable in the experiments enabling comparability of results in the usage of the verbs. The initial selection of verbs has been generated from a systematic analysis of the scientific literature and existing corpora for PPIs. We distinguish modifying interactions (MIs) such as posttranslational modifications (PTMs) from non-modifying interactions (NMIs) and assumed that MIs have a higher predictive capacity due to stronger scientific evidence proving the interaction. We found that MIs are less frequent in the corpus but can be extracted at the same precision levels as PPIs. A significant portion of correct PPI reportings in the BioCreAtIve II corpus use the verb associate, which semantically does not prove a relation. The performance of every monitored verb is listed and allows the selection of specific verbs to improve the performance of PPI extraction solutions. Programmatic access to the text processing modules is available online (www.ebi.ac.uk/webservices/whatizit/info.jsf) and the full analysis of Med line abstracts will be made through the Web pages of the Rebholz group. (C) 2009 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available