@inproceedings{islamaj-dogan-etal-2017-biocreative,
title = "{B}io{C}reative {VI} Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations",
author = "Islamaj Do{\u{g}}an, Rezarta and
Chatr-aryamontri, Andrew and
Kim, Sun and
Wei, Chih-Hsuan and
Peng, Yifan and
Comeau, Donald and
Lu, Zhiyong",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2321",
doi = "10.18653/v1/W17-2321",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
%A Islamaj Doğan, Rezarta
%A Chatr-aryamontri, Andrew
%A Kim, Sun
%A Wei, Chih-Hsuan
%A Peng, Yifan
%A Comeau, Donald
%A Lu, Zhiyong
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F islamaj-dogan-etal-2017-biocreative
%X 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.
%R 10.18653/v1/W17-2321
%U https://aclanthology.org/W17-2321
%U https://doi.org/10.18653/v1/W17-2321
%P 171-175
Markdown (Informal)
[BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations](https://aclanthology.org/W17-2321) (Islamaj Doğan et al., BioNLP 2017)
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.