BioCreative: Critical Assessment of Information Extraction in Biology is a community-wide effort for
evaluating text mining and information extraction systems applied to the
biological domain. Built on the success of the previous BioCreative Challenge
Evaluations and Workshops (BioCreative
I, II, II.5, III, 2012 workshop, and IV) the BioCreative Organizing Committee will host the BioCreative V Challenge in Sevilla, Spain on September 9-11, 2015.
goal of BioCreative is the active involvement of the text mining user community
in the design of the tracks, preparation of corpus and the testing of
interactive systems. For BioCreative V, the selection of the tracks has been
driven in part by suggestions from the biocuration community, by our goal of
addressing interoperability -- a major barrier to adoption to text mining tools
--, and via an open call for tasks of interest to both the bioNLP and user
Collaborative Biocurator Assistant: Development of BioC-compatible
modules which complement each other and an integrated system that assists
BioGRID curators. A non-competitive, cooperative task in which participants
work together to build a better system.
Identification of chemical compounds and of gene and protein mentions in
medicinal chemistry patents
Chemical-disease relation: Automatic detection of
chemical/drugs and diseases, and their relations in PubMed abstracts. In
particular, the CDR task focuses on extracting the relationship of drug-induced
Extraction of causal network
information in Biological Expression Language: Text mining solutions to develop and test
novel approaches for relation extraction in the context of pathway networks.
The goal is to assess the utility of such tools for the automated annotation
and network expansion, and their suitability as supporting tools for assisted
Interactive Curation: Demonstration and evaluation of
web-based systems addressing user-defined tasks, evaluated by curators on
performance and usability.
Literature curation and biological
importance and practical use of text mining and information extraction systems
for the extraction of biological and biomedical annotations and integration to
existing life sciences knowledgebases.
Crowdsourcing and text annotation for database
curation: Demonstration of real world cases of crowdsourcing
and community annotations for generating Gold Standard annotations for text
mining and information extraction systems and database annotations
Disease Annotation and connections
to the medical literature and clinical text mining: Use of text mining strategies to
extract clinically relevant information from public data and clinical records,
with a special focus on disease and symptoms related concepts.