The aim of this repo is to benchmark ML survival models (available via mlr3proba) on the TCGA PAAD dataset from the PanCancer Atlas project.

• TCGA data download and filter: tcga_paad.R
• Data preprocessing: preprocessing.R
• The scripts directory has several benchmarks, with some output results stored and the most important produced plots. The most important scripts/investigations are the following:
  • Benchmark CoxNet, Survival Trees and Survival Forests using nested-CV - script
  • Tuning strategy investigation (Random search vs Bayesian Optimization) using CoxNet and Survival Forests - script
  • XGBoost survival learner performance on mRNA dataset - script
  • CoxPH baseline performance using clinical features and several resampling strategies - script
  • CoxBoost (mRNA only and mRNA + clinical) vs CoxPH (clinical) - script
  • Glmboost survival learner performance on mRNA dataset - script
  • Wrapper-based Ensemble Feature Selection (eFS) per data modality - see script for mRNA data
  • Task powerset benchmark after eFS is applied (using simple CoxPH or multiple learners)