Programme

Approximate timing for a typical day is the following:
09:00 - 12:30           lectures
12:30 - 14:00           lunch
14:00 - 17:00/30      practical / exercises
17:00/30 - 19:00/30 free
19:00/30                   dinner

Sunday 19 November - Broad introduction and welcome dinner

Dr Frédéric Schütz, SIB Swiss Institute of Bioinformatics

17:00 - 18:00    Arrival of the participants
18:15                Informal welcome and presentation of the event (Grégoire Rossier, co-organizer, SIB Swiss Institute of Bioinformatics)
18:30                Broad machine learning introduction
19:30                "Round table" with participants' background and expectations
~20:15             Welcome dinner

Monday 20 November: Introduction to machine learning

Dr Frédéric Schütz, SIB Swiss Institute of Bioinformatics

Morning: Lectures

• Introduction to machine learning
• Supervised vs unsupervised learning
• Introduction to some classification and machine learning algorithms: k-means, LDA/QDA, Random forest, etc.
• Evaluating performance
  • generalization/overfitting
  • training, test sets
  • cross-validation, bootstrap, jackknife
  • Model selection
  • ROC curves

Afternoon: Exercises: machine learning with R.

Tuesday 21 November: Best practice in applied machine learning

Dr Eric Paquet, Computational Systems Biology, EPFL

Morning: lectures

• Pitfalls, experimental design and batch effect
• Diagnostic/QC plots in R
• PCA
• Clustering/heatmaps
• Boxplots
• Normalization
• Feature selections
• Regularization (lasso, ridge and elastic net)
• Neural networks (perceptron)
• Kernel trick (spectral)
• Reproducible research, Sweave, Jupyter notebooks, git
• Example of the MAQC II
• Example of applied machine learning in Systems Biology
• Cancer subtypes. How many subtypes? and identification
• HMM
• image analysis (drug discovery)
• image analysis (morphology classification)

Afternoon: exercises

• HMM
• Image analysis
• http://www.nature.com/articles/sdata201718 use this dataset.

Wednesday 22 November: Participants’ day

Morning: "participants, the floor is yours..."

• Lightning presentations
• Poster session
  see a detailed list below
  

Afternoon: Social activity

• visit of a glass factory, including fun activities.
  

Thursday 23 November: Machine Learning and metagenomics to study microbial communities

Dr Luis Pedro Coelho, EMBL, Heidelberg, Germany

Morning: lectures**

• Brief Introduction to microbial community wetlab technologies
• Presentation of important questions in the field
• Overview of raw data processing with NGLess tool
• Classification based on metagenomics-derived features
• Example based on Zeller et al., 2014: http://doi.org/10.15252/msb.20145645
• Feature normalization/filtering
• Biomarker discovery

**Lectures will be interactive based on Python & Jupyter notebooks

Afternoon: exercises

• Clustering for metagenomics: Metagenomic species, mOTUs, subspecies discovery…
• Machine learning for the exploration of community/environmental links:
• Example based on Sunagawa et al., 2015: http://doi.org/10.1126/science.1261359
• Different forms of ordination analysis
• Feature normalization for clustering
• Discussion of batch effects and techniques to minimize their impact on the final analysis
• Computer vision techniques for studying micro-eukaryotic communities

Friday 24 November : Deep learning in single-cell analysis

Dr María Rodríguez-Martínez, IBM Research Lab Zurich

Morning: lectures

• Introduction to deep learning
  • Why and how deep
  • Activations functions
  • Cost functions
  • Backpropagation
  • Regularization
  • Optimization
• Multi-Layer Perceptron (MLP)
• Auto-enconders (AE)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)

Afternoon: exercises

• Word Embeddings for molecular interaction inference (INtERAcT)
• Deep SWATH-MS, deep and unsupervised MS processing (DeepSWATH)
• Characterizing cell populations on single-cell data