Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.
Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5909924?pdf=render |
| _version_ | 1829501433999785984 |
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| author | Travers Ching Xun Zhu Lana X Garmire |
| author_facet | Travers Ching Xun Zhu Lana X Garmire |
| author_sort | Travers Ching |
| collection | DOAJ |
| description | Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have 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 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet. |
| first_indexed | 2024-12-16T09:18:26Z |
| format | Article |
| id | doaj.art-5c5445df31094e5a9e4e7f991fdca858 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T09:18:26Z |
| publishDate | 2018-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5c5445df31094e5a9e4e7f991fdca8582022-12-21T22:36:51ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-04-01144e100607610.1371/journal.pcbi.1006076Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.Travers ChingXun ZhuLana X GarmireArtificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have 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 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.http://europepmc.org/articles/PMC5909924?pdf=render |
| spellingShingle | Travers Ching Xun Zhu Lana X Garmire Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Computational Biology |
| title | Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. |
| title_full | Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. |
| title_fullStr | Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. |
| title_full_unstemmed | Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. |
| title_short | Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. |
| title_sort | cox nnet an artificial neural network method for prognosis prediction of high throughput omics data |
| url | http://europepmc.org/articles/PMC5909924?pdf=render |
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