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Overview of the gene regulation network and the bacteria biotope tasks in BioNLP'13 shared task

Overview of attention for article published in BMC Bioinformatics, June 2015
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Title
Overview of the gene regulation network and the bacteria biotope tasks in BioNLP'13 shared task
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/1471-2105-16-s10-s1
Pubmed ID
Authors

Robert Bossy, Wiktoria Golik, Zorana Ratkovic, Dialekti Valsamou, Philippe Bessières, Claire Nédellec

Abstract

We present the two Bacteria Track tasks of BioNLP 2013 Shared Task (ST): Gene Regulation Network (GRN) and Bacteria Biotope (BB). These tasks were previously introduced in the 2011 BioNLP-ST Bacteria Track as Bacteria Gene Interaction (BI) and Bacteria Biotope (BB). The Bacteria Track was motivated by a need to develop specific BioNLP tools for fine-grained event extraction in bacteria biology. The 2013 tasks expand on the 2011 version by better addressing the biological knowledge modeling needs. New evaluation metrics were designed for the new goals. Moving beyond a list of gene interactions, the goal of the GRN task is to build a gene regulation network from the extracted gene interactions. BB'13 is dedicated to the extraction of bacteria biotopes, i.e. bacterial environmental information, as was BB'11. BB'13 extends the typology of BB'11 to a large diversity of biotopes, as defined by the OntoBiotope ontology. The detection of entities and events is tackled by distinct subtasks in order to measure the progress achieved by the participant systems since 2011. This paper details the corpus preparations and the evaluation metrics, as well as summarizing and discussing the participant results. Five groups participated in each of the two tasks. The high diversity of the participant methods reflects the dynamism of the BioNLP research community. The evaluation results suggest new research directions for the improvement and development of Information Extraction for molecular and environmental biology. The Bacteria Track tasks remain publicly open; the BioNLP-ST website provides an online evaluation service, the reference corpora and the evaluation tools.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 7%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 40%
Student > Master 4 13%
Student > Ph. D. Student 3 10%
Student > Doctoral Student 2 7%
Professor 2 7%
Other 3 10%
Unknown 4 13%
Readers by discipline Count As %
Computer Science 8 27%
Agricultural and Biological Sciences 4 13%
Medicine and Dentistry 3 10%
Linguistics 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Other 6 20%
Unknown 5 17%

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
#20,283,046
of 22,817,213 outputs
Outputs from BMC Bioinformatics
#6,855
of 7,284 outputs
Outputs of similar age
#219,962
of 263,947 outputs
Outputs of similar age from BMC Bioinformatics
#103
of 109 outputs
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