Hybrid Curation of Gene-mutation Relations Combining Automated Extraction and Crowdsourcing

February 2014
Topics: Artificial Intelligence, Genetics, Genetic Engineering
John D. Burger, The MITRE Corporation
Emily Doughty, Stanford University School of Medicine
Ritu Khare, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
Chih-Hsuan Wei, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
Rajashree Mishra, The University of Maryland Baltimore County
John Aberdeen, The MITRE Corporation
David Tresner-Kirsch, The MITRE Corporation
Ben Wellner, The MITRE Corporation
Maricel G. Kann, The University of Maryland Baltimore County
Zhiyong Lu, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
Dr. Lynette Hirschman, The MITRE Corporation
Download PDF (308.32 KB)

This paper describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques are applied to extract gene-mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene-mutation-disease findings as an open source resource for personalized medicine.​

This article received MITRE's Ronald Fante Best Paper Award for the most significant peer-reviewed publication by MITRE staff in 2014. The award commemorates the late Dr. Ronald Fante, a MITRE Fellow and a highly respected scientist and prolific author.

The complete paper can be accessed on the Oxford Journals Database site. The attached PDF includes all appendices for the study.

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