Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data
By Travers Ching, Xun Zhu, Lana X. Garmire. PLoS Comput. Biol., 2018
399 citations recorded by the source corpus.
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.
Subfields: Cancer Research, Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/0a9fd9a4b5408241f1e63c7c8481e492f597c235
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Citations in the corpus
- The Application of Deep Learning in Cancer Prognosis Prediction (2020, methodology): “, 2018 [59] 10 types of cancer TCGA gene expression data, clinical data and survival data 5031 NN Input normalization and log-transformed, 0–2 hidden layers (143 nodes) Survival time L1, L2, or MCP 2 regularization, tanh activation for hidden layer(s), dropout, Cox regression as output layer 5-fold ”
- Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “DL applied to genomic, transcriptomic and other data types has the potential to predict prognosis and patient survival [59 – 62 , 112].”