RT Journal Article SR Electronic T1 Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data JF bioRxiv FD Cold Spring Harbor Laboratory SP 093021 DO 10.1101/093021 A1 Travers Ching A1 Xun Zhu A1 Lana X. Garmire YR 2016 UL http://biorxiv.org/content/early/2016/12/11/093021.abstract AB 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.