Module 3: How to make valid prognostic models with gene expression signatures?

Revision as of 10:19, 26 February 2015 by Sven (talk | contribs)
  • Title: "How to make valid prognostic models with gene expression signatures?"
  • Paper to be examined: “Gene Expression Profiling for Survival Prediction in Pediatric Rhabdomyosarcomas: A Report From the Children's Oncology Group”, J Clin Oncol. 2010 Mar 1;28(7):1240-6 (2010)[1]
  • Key claim of the paper: "Metagenes to discriminate patients with good prognosis from those with poor prognosis, with the potential to direct risk-adapted therapy."
  • Data and Code
  • Schedule:
    • H1: General introduction to the paper/motivation
    • H2-3: Write code to import the data and start computing "meta-genes"
    • H4-6: Aim to fit a predictive model for clinical outcome based on meta-genes
    • H7: Discussion: “Is a model that fits the data necessarily a good predictive model?”
    • H8: Sketch cross-validation approach
    • H9: Summarize results (e.g. on this wiki)
  • Key bioinformatics concept of this module:
    • Prognostic models
    • cross validation