Improved survival analysis by learning shared genomic information from pan-cancer data.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jul 1, 2020
Abstract
MOTIVATION: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.