Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

by Travers Ching, Xun Zhu, Lana X. Garmire

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 prognosi s 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-nne t 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 thr oughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

Source: PLoS Computational Biology - April 10, 2018 Category: Biology Authors: Travers Ching Source Type: research