Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

Travers Ching; Xun Zhu; Lana X. Garmire

doi:10.1371/journal.pcbi.1006076

Loading metrics

Open Access

Peer-reviewed

Research Article

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

Travers Ching,

Roles Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America
⨯
Xun Zhu,

Roles Methodology, Software

Affiliations Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America
⨯
Lana X. Garmire

Roles Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: lgarmire@cc.hawaii.edu

Affiliations Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America

http://orcid.org/0000-0002-4654-2126

⨯

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

Travers Ching,
Xun Zhu,
Lana X. Garmire

Published: April 10, 2018
https://doi.org/10.1371/journal.pcbi.1006076

About the Authors

Travers Ching

Roles Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America

Xun Zhu

Roles Methodology, Software

Lana X. Garmire

Roles Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: lgarmire@cc.hawaii.edu

http://orcid.org/0000-0002-4654-2126

Competing Interests

The authors have declared that no competing interests exist.

Subject Areas
?

For more information about PLOS Subject Areas, click here.
We want your feedback. Do these Subject Areas make sense for this article? Click the target next to the incorrect Subject Area and let us know. Thanks for your help!

Artificial neural networks
Is the Subject Area "Artificial neural networks" applicable to this article?

Thanks for your feedback.
Renal cancer
Is the Subject Area "Renal cancer" applicable to this article?

Thanks for your feedback.
Gene expression
Is the Subject Area "Gene expression" applicable to this article?

Thanks for your feedback.
Prognosis
Is the Subject Area "Prognosis" applicable to this article?

Thanks for your feedback.
Trees
Is the Subject Area "Trees" applicable to this article?

Thanks for your feedback.
Neural networks
Is the Subject Area "Neural networks" applicable to this article?

Thanks for your feedback.
Hepatocellular carcinoma
Is the Subject Area "Hepatocellular carcinoma" applicable to this article?

Thanks for your feedback.
Insulin signaling
Is the Subject Area "Insulin signaling" applicable to this article?

Thanks for your feedback.