Topic outline

  • General

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    Machine Learning for bioinformatics and computational biology

    Zurich, 21-25 November 2016

    Location: University of Zurich, Irchel campus, room Y55-L-06/08

    This page is addressed to registered participants. To access the course description and the application form, please click here.

    For any assistance, please contact training@sib.swiss.

  • Technical / software prerequisite

    Laptop with recent versions of R installed, 3 GB of free disk space

    You need to install MXNET (an R package for deep learning) BEFORE the course. The installation instructions can be found here: http://mxnet.io/get_started/setup.html

  • General Programme

    Days 1 & 2: 9h - 17h
    Introduction to Machine Learning: concepts and methods
    Dr Frédéric Schütz, University of Lausanne and Bioinformatics Core Facility Group, SIB Swiss Institute of Bioinformatics, Lausanne.

    Days 3 to 5: Applications with use cases:

    Day 3: 9h - 17h
    Deep learning methods and cancer genomics
    Theory: Introduction to deep learning, standard architectures: MLP, autoencoders, recurrent nets.
    Practical: use of standard data sets and application to genomic data
    The day will be composed of an alternance of teaching and exercises
    Prof Ivo Kwee, Bioinformatics Core Unit, SIB and Institute of Oncology Research, Bellinzona, Switzerland

    Day 4: 9h - 17h
    Feature selection for biomarker discovery from high-content -omics data
    Prof Carlos Peña-Reyes, Computational Intelligence for Computational Biology, HEIG-VD/SIB Swiss Institute of Bioinformatics, Yverdon, Switzerland

    Day 5: 9h - 17h
    Machine Learning and metagenomics to study microbial communities
    Dr Luis Pedro Coelho, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.