The most detailed and up-to-date description of track 3 can be found on the openbel wiki at BioCreative VI Track 3 (BEL Task 2017) Home.

The rest of this page contains only preliminary information.

Overview

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. In BioCreative V, we tackled this complexity by extracting causal relationships represented in Biological Expression Language (BEL, www.openbel.org). 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 problem:

1. Given selected textual evidence, construct the corresponding BEL statement
2. Given a BEL statement, detect all available textual evidence

The description of the BioCreative V task from 2015, the training data and links to the papers and to the evaluation website can be found under BEL Track Task Description

Updated information about the BioCreative VI task will become available soon. Currently, new test data for Biocreative 2017 is created. This year, we focus on the disease Ulcerative Colitis. Since new biological processes might play a role in this disease, the list of biological processes will be updated and may contain new biolobical processes compared to BioCreative V. The will be published as soon as the annotation of the test sets is finished.

Dates

Please note that the dates are indicative only and subject to change
Training-2015 + Sample-2015 + Test-2015 data: Already available here
Evaluation website: here
Release test data: Jul 24, 2017 (Mon) (Jul 11, 2017 (Tue))
Submission of results (by participants) Deadline: Jul 25, 2017 (Tue) (Jul 12, 2017 (Wed))
Release of gold standard entities: Jul 26, 2017 (Wed)(Jul 13, 2017 (Thu))
Second submission deadline: Jul 27, 2017 (Thu) (Jul 14, 2017 (Fri)) (optional delivery of revised results of task 1 including gold standard entities)
Notification of results to participants: Aug 18, 2017 (Fri) (Aug 4, 2017 (Fri)) (results of task 1 might be notified earlier)
Submission of the papers: Sep 3, 2017 (Sun) (Aug 20, 2017 (Sun))
Provide feedback on the papers: September 15, 2017 (Fri)
Camera-ready: October 1, 2017 (Sun)
Workshop: October 18-20, 2017 (Wed-Fri)

BEL documentation and task description

An introduction to the BEL language structure can be found at the OpenBEL website and at the BEL wiki. The description of the corpora and annotation guidelines are published in [1]. The BioCreative V task and the evaluation results are presented in [2]. The training data was mainly generated from data published in the Causal biological network database [3]. As part of an on-going product assessment program, the sbvIMPROVER initiative is supporting the manual curation and expansion of biological networks related to human lung disease [4-9]. A large-scale crowdsourcing verification approach for the verification and enhancement of these biological networks, called Network Verification Challenge (NVC) [10], was organized by them. Further data was provided by Selventa, the comany originally invented BEL. Based on these resources, training and test data for the BioCreative V BEL track was extracted. For the BioCreative VI task, test data will be newly annotated.

Task organizing committee:

Dr. Juliane Fluck (Fraunhofer Institute SCAI, Germany)
Sumit Madan (Fraunhofer Institute SCAI, Germany)
Dr. Justyna Szostak (Philip Morris International: PMI, Switzerland)
Prof. Dr. Martin Hofmann-Apitius (OpenBEL Consortium, Germany)

References:

1. Fluck, J., Madan, S., Ansari, S., et al. Database (Oxford)., Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

2. Rinaldi, F., Effendorf, T., Madan, S., et al. Database (Oxford)., BioCreative 5 Track 4: A Shared Task for the Extraction of Causal Network Information in Biological Expression Language.

3. Boué S, Talikka M, Westra JW, Hayes W, Di Fabio A, Park J, Schlage WK, Sewer A, Fields B, Ansari S (2015) Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database 2015: bav0301.

4. De Leon, H., Boue, S., Schlage, W.K., Boukharov, N., Westra, J.W., Gebel, S., VanHooser, A., Talikka, M., Fields, R.B., Veljkovic, E. et al. (2014) A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. Journal of Translational Medicine, 12, 185.

5. Gebel, S., Lichtner, R.B., Frushour, B., Schlage, W.K., Hoang, V., Talikka, M., Hengstermann, A., Mathis, C., Veljkovic, E., Peck, M. et al. (2013) Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence. Bioinformatics and Biology Insights, 7, 97-117.

6. Park, J.S., Schlage, W.K., Frushour, B.P., Talikka, M., Toedter, G. , Gebel, S., Deehan, R., Veljkovic, E., Westra, J.W., Peck, M.J., Boue, S., Kogel, U., Gonzalez-Suarez, I., Hengstermann, A., Peitsch, M.C., Hoeng, J. (2013) Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung. Journal of Clinical Toxicology, S12, 002.

7. Schlage, W.K., Westra, J.W., Gebel, S., Catlett, N.L., Mathis, C., Frushour, B.P., Hengstermann, A., Van Hooser, A., Poussin, C., Wong, B. et al. (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC systems biology, 5, 168.

8. Westra, J.W., Schlage, W.K., Frushour, B.P., Gebel, S., Catlett, N.L., Han, W., Eddy, S.F., Hengstermann, A., Matthews, A.L., Mathis, C. et al. (2011) Construction of a Computable Cell Proliferation Network Focused on Non-Diseased Lung Cells. BMC systems biology, 5, 105.

9. Westra, J.W., Schlage, W.K., Hengstermann, A., Gebel, S., Mathis, C., Thomson, T., Wong, B., Hoang, V., Veljkovic, E., Peck, M. et al. (2013) A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue. Bioinformatics and Biology Insights, 7, 167-192.

10. sbv IMPROVER project team (in alphabetical order)., Boue S, Fields B, Hoeng J, Park J, Peitsch MC, Schlage WK, Talikka M; Challenge Best Performers (in alphabetical order)., Binenbaum I, Bondarenko V, Bulgakov OV, Cherkasova V,Diaz-Diaz N, Fedorova L, Guryanova S, Guzova J, Igorevna Koroleva G, Kozhemyakina E, Kumar R, Lavid N, Lu Q, Menon S, Ouliel Y, Peterson SC, Prokhorov A, Sanders E, Schrier S, Schwaitzer Neta G, Shvydchenko I, Tallam A, Villa-Fombuena G, Wu J, Yudkevich I, Zelikman M. Enhancement of COPD biological networks using a web-based collaboration interface. Version 1. F1000Res. 2015 Jan 29;4:32. doi: 10.12688/f1000research.5984.1.