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Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data

Travers Ching, Xun Zhu, Lana X. Garmire
doi: https://doi.org/10.1101/093021
Travers Ching
*Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822, USA
†Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
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Xun Zhu
*Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822, USA
†Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
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Lana X. Garmire
*Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822, USA
†Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
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Abstract

Artificial neural networks (ANN) are computing architectures with massively parallel interconnections of simple neurons and has been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In over 10 TCGA RNA-Seq data sets, Cox-nnet achieves a statistically significant increase in predictive accuracy, compared to the other three methods including Cox-proportional hazards (Cox-PH), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, from both pathway and gene levels. The outputs from the hidden layer node can provide a new approach for survival-sensitive dimension reduction. In summary, we have developed a new method for more accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at github.com/lanagarmire/cox-nnet.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 11, 2016.
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Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data
Travers Ching, Xun Zhu, Lana X. Garmire
bioRxiv 093021; doi: https://doi.org/10.1101/093021
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Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data
Travers Ching, Xun Zhu, Lana X. Garmire
bioRxiv 093021; doi: https://doi.org/10.1101/093021

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