An end-to-end deep learning architecture for extracting protein–protein interactions affected by genetic mutations-Reference-Cited by-同舟云学术

An end-to-end deep learning architecture for extracting protein–protein interactions affected by genetic mutations

Published:2018-01-01 Issue: Volume:2018 Page:
ISSN:1758-0463
Container-title:Database
language:en
Short-container-title:

Author:

Tran Tung¹^ORCID,Kavuluru Ramakanth¹²

Affiliation:

1. Department of Computer Science, University of Kentucky, Lexington, KY, USA

2. Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA

Funder

U.S. National Library of Medicine

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

Link

http://academic.oup.com/database/article-pdf/doi/10.1093/database/bay092/25811124/bay092.pdf

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4. A shortest path dependency kernel for relation extraction.;Bunescu;Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing,2005

5. Qingyu,C., Panyam,N.C., Elangovan, A.et al. (2017) Document triage and relation extraction for protein-protein interactions affected by mutations. In: BioCreative VI Workshop, pp. 102–105.

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