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greenelab / deep-review

A collaboratively written review paper on deep learning, genomics, and precision medicine
https://greenelab.github.io/deep-review/
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Cox-nnet: an artificial neural network Cox regression for prognosis prediction #155

Open agitter opened 7 years ago

agitter commented 7 years ago

http://doi.org/10.1101/093021

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 offers richer biological information, from both pathway and gene levels. The outputs from the hidden layer node can be utilized as a new approach for survival-sensitive dimension reduction. In summary, we have developed a new method for more accurate and efficient prognosis prediction, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

By @traversc

traversc commented 7 years ago

Thanks for posting this! I wouldn't necessarily say this fits under deep learning, since I used a single hidden layer. I didn't observe any performance increase in using multiple fully connected hidden layers.