Difference between revisions of "Module 3: How to make valid prognostic models with gene expression signatures?"

 
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====  '''MODULE 3'  LESSON PLAN  ''  ====
 
====  '''MODULE 3'  LESSON PLAN  ''  ====
  
* OCT 2015
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* Title: "How to make valid prognostic models with gene expression signatures?"
:* 26/10/2015: INTRODUCTION  TO THE COURSE
 
  
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* 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)[http://jco.ascopubs.org/content/28/7/1240.long]
  
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* 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."
  
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* Data and Code
 +
 
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* Schedule:
  
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** H1: General introduction to the paper/motivation
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** 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
  
* contact: mauro.delorenzi@unil.ch
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** H7: Discussion: “Is a model that fits the data necessarily a good predictive model?”
 +
** H8: Sketch cross-validation approach
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** H9: Summarize results (e.g. on this wiki)
 +
 
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* Key bioinformatics concept of this module:
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** Prognostic models
 +
** cross validation
 +
 
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* back to [[UNIL MSc course: "Forensics in Bioinformatics 2015"]]

Revision as of 10:19, 26 February 2015

'MODULE 3' LESSON PLAN

  • 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