Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America.
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America.
PLoS Comput Biol. 2018 Apr 10;14(4):e1006076. doi: 10.1371/journal.pcbi.1006076. eCollection 2018 Apr.
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.
人工神经网络 (ANN) 是一种具有许多相互连接的简单神经启发式计算元素的计算架构，已应用于生物医学领域，如成像分析和诊断。我们开发了一种称为 Cox-nnet 的新 ANN 框架，用于从高通量转录组学数据预测患者预后。在 10 个 TCGA RNA-Seq 数据集上，Cox-nnet 的预测准确性与其他方法（包括 Cox 比例风险回归（具有 LASSO、岭和 mimimax 凹惩罚）、随机森林生存和 CoxBoost）相同或更好。Cox-nnet 还揭示了更丰富的生物学信息，包括途径和基因水平。隐藏层节点的输出为生存敏感降维提供了一种替代方法。总之，我们开发了一种针对高通量数据的准确有效的预后预测新方法，具有功能生物学见解。源代码可在 https://github.com/lanagarmire/cox-nnet 上免费获得。
