@article {Ching093021, author = {Travers Ching and Xun Zhu and Lana X. Garmire}, title = {Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data}, elocation-id = {093021}, year = {2016}, doi = {10.1101/093021}, publisher = {Cold Spring Harbor Laboratory}, 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.}, URL = {https://www.biorxiv.org/content/early/2016/12/11/093021}, eprint = {https://www.biorxiv.org/content/early/2016/12/11/093021.full.pdf}, journal = {bioRxiv} }