Predicting Influenza A Tropism with End-to-End Learning of Deep Networks-Reference-Cited by-同舟云学术

Predicting Influenza A Tropism with End-to-End Learning of Deep Networks

Published:2019-12-01 Issue:6 Volume:17 Page:468-476
ISSN:2326-5094
Container-title:Health Security
language:en
Short-container-title:Health Security

Author:

Scarafoni Dan¹,Telfer Brian A.²,Ricke Darrell O.³,Thornton Jason R.⁴,Comolli James⁵

Affiliation:

1. Dan Scarafoni, MS, is a graduate student, Lab for Computational Behavior Analysis, Georgia Institute of Technology, Atlanta, GA.

2. Brian A. Telfer, PhD, is a Senior Staff Member, Human Health and Performance Systems Group

3. Darrell O. Ricke, PhD, is on the Technical Staff, Biological and Chemical Technologies

4. Jason R. Thornton, PhD, is Associate Group Leader, Informatics and Decision Support Group

5. James Comolli, PhD, is on the Technical Staff, Biological and Chemical Technologies Group; all at the MIT Lincoln Laboratory, Lexington MA.

Publisher

Mary Ann Liebert Inc

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Safety Research,Emergency Medicine,Health (social science)

Link

https://www.liebertpub.com/doi/pdf/10.1089/hs.2019.0055

Reference27 articles.

1. Distinct Host Tropism Protein Signatures to Identify Possible Zoonotic Influenza A Viruses

2. Predicting host tropism of influenza A virus proteins using random forest

3. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

4. The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus

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