期刊
PLOS COMPUTATIONAL BIOLOGY
卷 14, 期 4, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1006076
关键词
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资金
- NIEHS through funds by the trans-NIH Big Data to Knowledge (BD2K) initiative [K01ES025434]
- P20 COBRE by NIH/NIGMS [GM103457]
- NLM [R01 LM012373]
- NICHD [R01 HD084633]
- Hawaii Community Foundation Medical Research Grant [14ADVC-64566]
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/langermire/cox-nnet.
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