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 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.Author summary: The increasing application of high-througput transcriptomics data to predict patient prognosis demands modern computational methods. With the re-gaining popularity of artificial neural networks, we asked if a refined neural network model could be used to predict patient survival, as an alternative to the conventional methods, such as Cox proportional hazards (Cox-PH) methods with LASSO or ridge penalization. To this end, we have developed a neural network extension of the Cox regression model, called Cox-nnet. It is optimized for survival prediction from high throughput gene expression data, with comparable or better performance than other conventional methods. More importantly, Cox-nnet reveals much richer biological information, at both the pathway and gene levels, by analyzing features represented in the hidden layer nodes in Cox-nnet. Additionally, we propose to use hidden node features as a new approach for dimension reduction during survival data analysis.