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        <title>Extraction of Gene-Environment Interaction from the Biomedical Literature</title>
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    <abstract>Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.</abstract>
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%0 Conference Proceedings
%T Extraction of Gene-Environment Interaction from the Biomedical Literature
%A You, Jinseon
%A Chung, Jin-Woo
%A Yang, Wonsuk
%A Park, Jong C.
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F you-etal-2017-extraction
%X Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.
%U https://aclanthology.org/I17-1087/
%P 865-874

Markdown (Informal)

[Extraction of Gene-Environment Interaction from the Biomedical Literature](https://aclanthology.org/I17-1087/) (You et al., IJCNLP 2017)

• Extraction of Gene-Environment Interaction from the Biomedical Literature (You et al., IJCNLP 2017)

ACL

• Jinseon You, Jin-Woo Chung, Wonsuk Yang, and Jong C. Park. 2017. Extraction of Gene-Environment Interaction from the Biomedical Literature. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 865–874, Taipei, Taiwan. Asian Federation of Natural Language Processing.