Difference between revisions of "Module 3: How to make valid prognostic models with gene expression signatures?"
Line 1: | Line 1: | ||
==== '''MODULE 3' LESSON PLAN '' ==== | ==== '''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)[http://jco.ascopubs.org/content/28/7/1240.long] | ||
+ | * 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 | ||
+ | |||
+ | * back to [[UNIL MSc course: "Forensics in Bioinformatics 2015"]] |
Revision as of 11:19, 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