[Corpora-List] Second call for participation BioCreative VI

Juliane Fluck juliane.fluck at scai.fraunhofer.de
Thu May 11 17:37:40 CEST 2017

Hi all,

We are very happy to let you know that BioCreative VI (www.biocreative.org) is underway, with many interesting tracks! Some tracks are ready to release the training sets so get ready!

BioCreative VI Challenge and Workshop

October 18-20, DoubleTree by Hilton Hotel, Bethesda, Maryland USA

Team registration <http://www.biocreative.org/events/biocreative-vi/team/> for tracks is now open!

Workshop registration will start mid June

BioCreative: Critical Assessment of Information Extraction in Biology (http://www.biocreative.org/ <http://www.biocreative.org/%20>) is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. These are the tracks for BioCreative VI:

* *Track 1:*


Interactive Bio-ID Assignment (IAT-ID) Track on innovations in

Biomedical Digital Curation

/Organizers: Lynette Hirschman, Cecilia Arighi, Thomas Lemberger and

Cathy Wu/

The Bio-ID track will explore the ID assignment to selected

bioentities both at the pre- and post-publication stages, with the

aim of facilitating downstream article curation. To do this we are

bringing together the various stakeholders to discuss functional

requirements and develop interoperable digital curation tools. Built

on previous BioCreative experiments, including the interactive

tracks, the BioC, gene/protein/chemical extraction tracks, and

BeCalm framework, the task is designed to foster the development of

an integrated and interoperable workflow of multiple text mining

tools for real-world testing in pilot publishing frameworks.

More information about this track can be found at


* *Track 2:*


Text-mining services for Kinome Curation

/Organizers: Julien Gobeill, Patrick Ruch and Pascale Gaudet/

Literature triage (selection of relevant articles for curation) is a

basic task performed by virtually all curated molecular biology

databases. This task will focus on triage for both Protein-Disease

and Protein-GO annotations related to human kinases. The full data

set covers a significant fraction of the Human Kinome (300 proteins

out ~500 kinases), with 30,000 annotations from 13,000 articles

ready to be integrated in the neXtProt database by 2017. It contains

comprehensive annotations about kinase substrates, GO Biological

Processes and Diseases. Each annotation is provided with a PMID.

The first two tasks deal with triage of abstracts or full-texts. The

third task deals with passage selection: given a kinase, an axis,

and a full-text regarded as relevant after SIB curation, the systems

will return a snippet of max. 500 characters containing enough

information to make an annotation.

More information about this track can be found at


* *Track 3:*


Extraction of causal network information using the Biological

Expression Language (BEL)

/Organizer: Juliane Fluck and Sumit Madan/

Automatic extraction of biological network information is one of the

most desired and most complex tasks in biological and medical text


In BioCreative V, we tackled this complexity by extracting causal

relationships represented in Biological Expression Language (BEL).

BEL is an advanced knowledge representation format which has been

designed to be both human readable and machine processable. The

smallest unit is a BEL statement or BEL nanopub, expressing a single

causal relationship. In the last BioCreative, there was only a

limited time for participants to train on the data and, in addition,

the evaluation environment became only available for the test phase.

Furthermore, for the second subtask, the sentence classification, no

training data was available. Therefore, we decide to present the

same task based on new test data. This time, the training data for

both subtask is available and, the evaluation environment can be

used during the training time. As before, the challenge is organized

into two tasks which will evaluate the complementary aspects of the


1-Given selected textual evidence, construct the corresponding

BEL statement

2-Given a BEL statement, detect all available textual evidence The description of the task, the training data and links to the

papers and to the evaluation website can be found under the

following URL:



* *Track 4:*

<http://www.biocreative.org/tasks/biocreative-vi/track-4/> Mining

protein interactions and mutations for precision medicine (PM)

/Organizers: Rezarta Islamaj Dogan, Andrew Chatr-aryamontri, Sun

Kim, Donald C. Comeau, Zhiyong Lu/

We aim to bring together the biomedical text mining community in a

new challenge for precision medicine, focusing on identifying and

extracting protein-protein interactions affected by mutations

described in the biomedical literature. Two subtasks are proposed:

*1-Document Triage:*

Identifying relevant PubMed citations describing genetic mutations

affecting protein-protein interactions

*2-Relation Extraction:*

Extracting PPI pairs experimentally verified to be affected by the

presence of a genetic mutation

Task datasets will be available in multiple formats (e.g. BioC) and consist of PubMed articles curated for BioGRID and other PPI databases. More information about this track can be found at http://www.biocreative.org/tasks/biocreative-vi/track-4/

* *Track 5:*

<http://www.biocreative.org/tasks/biocreative-vi/track-5/> Text

mining chemical-protein interactions

/Organizers: Martin Krallinger, Alfonso Valencia, Analia Lourenço/

Considerable work has been done on the detection of genes/proteins

and also chemical compound mentions, but despite the relevance of

relations between them for both biological and well as

pharmacological and clinical research only a limited number of

strategies have been published to detect interactions between them.

A range of different types chemical-protein/gene interactions are of

key relevance for biology, including metabolic relations (e.g.

substrates, products) inhibition, binding or induction associations.

Our aim is to promote research in this field, and to focus on

chemical-protein interactions that might be of relevance for

precision medicine as well as for drug discovery and basic

biomedical research. This task will consist of two subtasks:

*1- Chemical-protein interaction pair detection task:*

Extracting relations between chemical entities and protein/genes

belonging to at least one of a pre-defined set of relation types.

*2- Chemical-protein interaction type detection task:*

Providing for previously detected interaction pairs (of task 1) the

corresponding relation type qualifier).

Task training and test datasets will prepared and consist of abstracts curated for chemical entity and protein/gene mentions (including mention offsets) as well as relationships between them according to a predefined set of interaction types. More information about this track can be found at http://www.biocreative.org/tasks/biocreative-vi/track-5/


Teams can participate in one or more of these tracks. Team registration will continue until final commitment is requested by the individual tracks. To register a team go to the team registration page <http://www.biocreative.org/events/biocreative-vi/team/>


Cecilia Arighi on behalf of the BioCreative Organizing Committee

BioCreative Organizing Committee

* Cecilia Arighi, University of Delaware, USA

* Andrew Chatr-aryamontri, Institute for Research in Immunology and

Cancer, Université de Montréal, Canada

* Donald Comeau, National Center for Biotechnology Information (NCBI),


* Kevin Cohen, University of Colorado, USA

* Juliane Fluck, Fraunhofer Institute for Algorithms and Scientific

Computing SCAI, Germany

* Sumit Madan, Fraunhofer Institute for Algorithms and Scientific

Computing SCAI, Germany

* Rezarta Islamaj Dogan, National Center for Biotechnology Information


* Pascale Gaudet, Swiss Bioinformatics Institute, Switzerland

* Julien Gobeill, Swiss Bioinformatics Institute, Switzerland

* Lynette Hirschman, MITRE Corporation, USA

* Sun Kim, National Center for Biotechnology Information (NCBI), NIH, USA

* Martin Krallinger, Spanish National Cancer Centre, CNIO, Spain

* Zhiyong Lu, National Center for Biotechnology Information (NCBI),


* Fabio Rinaldi, Swiss Bioinformatics Institute, Switzerland

* Patrick Ruch, Swiss Bioinformatics Institute, Switzerland

* Alfonso Valencia, Spanish National Cancer Centre, CNIO, Spain

* Cathy Wu, University of Delaware and Georgetown University, USA

-- ----------------------------------------------------------------------------- Dr. Juliane Fluck

Head of Text Mining Deputy Head of the Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53754 Sankt Augustin, Germany

Tel: +49 - 2241 - 14 - 2188 Fax: +49 - 2241 - 14 - 2656 E-mail: juliane.fluck at scai.fhg.de www: http://www.scai.fraunhofer.de

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