<p>Building deep learning models for evidence classification from the open access biomedical literature.</p>

Gully A. Burns; Xiangci Li; Nanyun Peng

doi:10.7490/f1000research.1116552.1

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Burns GA, Li X and Peng N. Building deep learning models for evidence classification from the open access biomedical literature. [version 1; not peer reviewed]. F1000Research 2019, 8:418 (poster) (https://doi.org/10.7490/f1000research.1116552.1)

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Building deep learning models for evidence classification from the open access biomedical literature.

Gully A. Burns¹, Xiangci Li, Nanyun Peng

Published 09 Apr 2019 (https://doi.org/10.7490/f1000research.1116552.1)

Author Affiliations

¹ Chan Zuckerberg Initiative, USA

Copyright © 2019 Burns GA et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 09 Apr 2019

Building deep learning models for evidence classification from the open access biomedical literature.
[version 1; not peer reviewed]

Gully A. Burns¹, Xiangci Li, Nanyun Peng

Author Affiliations

¹ Chan Zuckerberg Initiative, USA

Presented at

The 12th International Biocuration Conference 2019

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Biocuration, deep Learning, evidence, method detection, document triage, molecular interactions.

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