The NVC3 Challenge

The 3rd sbv IMPROVER Network Verification Challenge (“NVC3”) aims to verify and enhance three (one for each metabolic phase) unpublished causal biological network models describing the signaling pathways leading to the activation of enzymes involved in the three phases of liver xenobiotic metabolism.

The networks will be released for editing sequentially and the challenge will be open until 2018.


Compete. Collaborate. Contribute.

  • Join your peers as they unite to verify and enhance existing biological network models that will then be released to the community for use in research applications such as drug discovery, personalized medicine, and toxicological assessment.

  • Collaborate: have fun competing and collaborating with others.

  • Test and expand your knowledge.

  • Learn the Biological Expression Language, and use BELIEF, a curation tool to create BEL statements from text extracted from scientific publications.

  • Challenge your peers and see in real time how you rank in the leaderboard.

  • A gift card of 150 USD will reward participants reaching 3000 points in the leaderboard (see Challenge rules).

  • At the end of the challenge the best performing participants will be rewarded with a travel grant of up to 2,000 USD (see Challenge rules).


Register now!

How to participate?

You can take part in the challenge in three different ways:

  • Participate on your own;
  • Participate as a team lead (e.g. on behalf of your institution or a group of colleagues) and invite others to join; or
  • Join an existing team.

Note: The best performers will be rewarded with gift cards and/or a travel grant, based on their individual performance rather than their team’s performance.


What to do?

A summary of the challenge is illustrated in the figure below.


Moreover, the following series of videos will give you in-depth information about the Network Verification Challenge:

  • BEL language and creation of network edges and nodes - The biological networks models included in the NVC have been scripted in the Biological Expression Language (BEL). You can learn BEL while interpreting the networks, and use it to add edges and nodes.
  • The online crowd-verification process - This tutorial on the online crowd-verification process describes the different types of actions on the biological network models that can be used for verification and to award reputation points.
  • Overview of the biological network models - This overview explains the biological network models that have been included in the NVC. It introduces the biological processes being modelled, the boundary conditions of the models, and the methodology used to build the models.
  • Webinars - A series of live sessions covering topics around this challenge. This allows you to deepen your knowledge and actively participate in discussions. Additionally, past webinars will be made available as videos.
  • More webinars.

In addition, on the Get started section, you can find step-by-step explanations on how to get started on the Challenge (create/manage an account, explore the networks, add edges and evidence, monitor activity, etc).


What to do with BELIEF?

The network models are encoded in the Biological Expression Language (BEL) which represents scientific knowledge in human readable and computable format as semantic triples (subject, a predicate, and an object).

Here is an example:

 

Since neither purely manual nor automated literature curation yields satisfactory results, in terms of curation time and quality, we have developed the BEL Information Extraction workFlow (BELIEF)1-5. BELIEF is a semi-automated workflow for BEL network creation. It embeds an information extraction workflow with state-of-the-art named entity recognition and relation extraction methods. BELIEF is now routinely used to build biological network models.

The causal biological network construction is divided in 3 steps:

  1. Literature review. Set of specific boundary criteria for the biological process of interest and selection of scientific articles
  2. Literature curation. Submission of the scientific articles to the text mining pipeline in the BELIEF platform. It automatically identifies causal relationships& proposes BEL statements. BELIEF platform also supports the user to check the validity of BEL statements and annotations.
  3. Network model review. The extracted causal relationships are then compiled into a causal network model. The nodes in the network model are connected by causally related edges.

You can leverage the BELIEF platform for extending the networks and creating evidences.

 

Instructions on how to Use BELIEF to curate Network Models

If you are a new user of BELIEF, please have a look at this step-by-step tutorial.


References:

  1. Fluck, J.; Klenner, A.; Madan, S.; Ansari, S.; Bobic, T.; Hoeng, J.; Hofmann-Apitius, M.; Peitsch, M.; (2013) “BEL networks derived from qualitative translations of BioNLP Shared Task annotations”. The Association for Computational Linguistics (ACL) Sofia 2013.
  2. Fluck, J.; Madan, S.; Ansari, S.; Szostak J.; Hoeng, J.; Zimmermann, M.; Hofmann-Apitius, M.; Peitsch, M.; (2014) “BELIEF - A semiautomatic workflow for BEL network creation. 6th International Symposium on Semantic Mining in Biomedicine“ (SMBM 2014), Aveiro, Portugal.
  3. Madan, S., Hodapp, S., & Fluck, J.; (2015) “BELIEF Dashboard - a Web-based Curation Interface to Support Generation of BEL Networks” In Proceedings of the Fifth BioCreative Challenge Evaluation Workshop (pp. 409–417). Sevilla, Spain.
  4. Szostak J, Ansari S, Madan S, Fluck J, Talikka M, Iskandar A, et al. (2015) ”Construction of biological networks from unstructured information based on a semi-automated curation workflow”. Database : the journal of biological databases and curation.
  5. Madan, S., Hodapp, S., Senger, P., Ansari, S., Szostak, J., Hoeng, J., Peitsch, M., & Fluck, J. (2016) “The BEL Information Extraction Workflow (BELIEF): Evaluation in the BioCreative V BEL and IAT track”. The Journal of Biological Databases and Curation (DATABASE)

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