BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
Rezarta Islamaj Doğan, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, Zhiyong Lu
Abstract
The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI). In order to support this track with an effective train-ing dataset with limited curator time, the track organizers carefully reviewed Pub-Med articles from two different sources: curated public PPI databases, and the re-sults of state-of-the-art public text mining tools. We detail here the data collection, manual review and annotation process and describe this training corpus charac-teristics. We also describe a corpus per-formance baseline. This analysis will provide useful information to developers and researchers for comparing and devel-oping innovative text mining approaches for the BioCreative VI challenge and other Precision Medicine related applica-tions.- Anthology ID:
- W17-2321
- Volume:
- BioNLP 2017
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 171–175
- Language:
- URL:
- https://aclanthology.org/W17-2321
- DOI:
- 10.18653/v1/W17-2321
- Cite (ACL):
- Rezarta Islamaj Doğan, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, and Zhiyong Lu. 2017. BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations. In BioNLP 2017, pages 171–175, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations (Islamaj Doğan et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-2321.pdf