Abstract
We describe a system that automatically extracts biological events from biomedical journal articles, and translates those events into Biological Expression Language (BEL) statements. The system incorporates existing text mining components for coreference resolution, biological event extraction and a previously formally untested strategy for BEL statement generation. Although addressing the BEL track (Track 4) at BioCreative V (2015), we also investigate how incorporating coreference resolution might impact event extraction in the biomedical domain. In this paper, we report that our system achieved the best performance of 20.2 and 35.2 in F-score for the full BEL statement level on both stage 1, and stage 2 using provided gold standard entities, respectively. We also report that our results evaluated on the training dataset show benefit from integrating coreference resolution with event extraction.
Highlights
Biological networks such as gene regulatory networks, signal transduction pathways and metabolic pathways capture a series of protein-protein interactions, or relationships between proteins and chemicals, which could explain complex biological processes underlying specific health conditions
To address Task 1, we developed a pipeline system which consists of the Turku Event Extraction System (TEES) [21], coupled with a coreference resolution component and an automatic system for generating Biological Expression Language (BEL) statements that has not previously been formally evaluated [22]
Once BEL statements that a system predicts are submitted, the result is evaluated on each level
Summary
Biological networks such as gene regulatory networks, signal transduction pathways and metabolic pathways capture a series of protein-protein interactions, or relationships between proteins and chemicals, which could explain complex biological processes underlying specific health conditions. There is much interest in standard representations of biological networks, such as the Biological pathway exchange language [4], the Systems Biology Markup Language [5] and the Biological Expression Language (BEL) [6]. Such representations in a structured syntax can support visualisation of biological systems, and computational modelling of these systems [7,8,9]. There have been community-wide efforts targeting biomedical event extraction since 2009, in a series of evaluations known as the BioNLP Shared Tasks [12, 26, 27]. The initial task in 2009 mainly focused on extraction of biomedical events involving genes and proteins. For the GENIA event extraction shared task, a state-of-the-art system (TEES) using machine learning methods achieved the best performance in the task 2009, and achieved robust performance in 2011 and 2013 [14, 21, 28]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Database : the journal of biological databases and curation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
