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Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013

Overview of attention for article published in BMC Bioinformatics, June 2015
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Title
Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/1471-2105-16-s10-s2
Pubmed ID
Authors

Sampo Pyysalo, Tomoko Ohta, Rafal Rak, Andrew Rowley, Hong-Woo Chun, Sung-Jae Jung, Sung-Pil Choi, Jun'ichi Tsujii, Sophia Ananiadou

Abstract

Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies. Six groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks. The results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Student > Ph. D. Student 8 15%
Student > Master 7 13%
Student > Bachelor 5 10%
Other 3 6%
Other 5 10%
Unknown 14 27%
Readers by discipline Count As %
Computer Science 17 33%
Engineering 5 10%
Medicine and Dentistry 5 10%
Linguistics 2 4%
Mathematics 1 2%
Other 4 8%
Unknown 18 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 July 2015.
All research outputs
#18,418,919
of 22,817,213 outputs
Outputs from BMC Bioinformatics
#6,314
of 7,284 outputs
Outputs of similar age
#189,577
of 263,947 outputs
Outputs of similar age from BMC Bioinformatics
#98
of 109 outputs
Altmetric has tracked 22,817,213 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.