@InProceedings{islamajdogan-EtAl:2017:BioNLP17,
  author    = {Islamaj Dogan, Rezarta  and  Chatr-aryamontri, Andrew  and  Kim, Sun  and  Wei, Chih-Hsuan  and  Peng, Yifan  and  Comeau, Donald  and  Lu, Zhiyong},
  title     = {BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {171--175},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2321}
}