Improved survival analysis by learning shared genomic information from pan-cancer data

Sunkyu Kim, Keonwoo Kim, Junseok Choe, Inggeol Lee, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

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. RESULTS: We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples. AVAILABILITY AND IMPLEMENTATION: Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)i389-i398
JournalBioinformatics (Oxford, England)
Volume36
Issue number1
DOIs
Publication statusPublished - 2020 Jul 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Improved survival analysis by learning shared genomic information from pan-cancer data'. Together they form a unique fingerprint.

Cite this