Section outline

  • 9h to 12h30 - Philipp Bucher, René Dreos, Giovanna Ambrosini and Kumar Sunil (SIB and EPFL) - Machine learning applications in clinical diagnosis

    Basic concepts and methods of machine learning will be illustrated with a real life application. As an example, we will use the Lung Cancer Diagnostic Signature Challenge organized by sbv Improver, see https://sbvimprover.com/challenge-1/challenge/lung-cancer.  During the practical, course participants will have the opportunity to test various combinations of feature selection methods, data reduction techniques, training algorithms and classifier types using the data provided by this challenge.

    Reading material: Tarca et al. (2013). Strengths and limitations of microarray-based phenotype prediction: Lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics. 29, 2892-2899.

    Software needed: R version 3.1.1 (version 3.1.2 is not supported) with the following packages installed: affy; affyio; gcrma; limma; GEOquery; hgu133plus2.db; pROC; nnet and maPredictDSC