Machine Learning for bioinformatics and systems biology 2021

Course date

11-15 October 2021, Amsterdam (if COVID-19 restrictions are lifted, otherwise online)

Course coordinator

Perry Moerland, Amsterdam UMC (location AMC)


  • Marcel Reinders (Delft University of Technology)
  • Lodewyk Wessels (Netherlands Cancer Institute)
  • Perry Moerland (Amsterdam UMC, location: Academic Medical Center)

Course credits

1.5 ECTS for following the course, 3 ECTS when successfully completing a final assignment

Course material

Find the course preparation materials here.

Course overview

Modern biology is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels. Interpretation of these large-scale biological data requires the detection of statistical dependencies and patterns in order to establish useful models of complex biological systems. Techniques from machine learning are key in this endeavour. Typical examples are the visualization of single-cell RNA-seq data using dimensionality reduction methods, base calling for nanopore sequencing data using hidden Markov models and (recurrent) neural networks, and classification of high-throughput microscopy image data using convolutional neural networks. In this one-week course, the foundations of machine learning will be laid out and commonly used methods for unsupervised (clustering, dimensionality reduction, visualization) and supervised (mainly classification) learning will be explained in detail. Methods will be illustrated using recent examples from the fields of systems biology and bioinformatics. Methods discussed in the morning lectures will be put into practice during the afternoon computer lab sessions.

Topics include:

  • Density estimation, including histograms, nearest neighbour, Parzen
  • Evaluation, including ROC, cross-validation
  • Parametric and non-parametric classifiers, including linear discriminant analysis, k-nearest neighbours, logistic regression, decision trees and random forests
  • Feature selection, including search algorithms (forward, backward, branch & bound) and sparse classifiers (ridge, lasso, elastic net)
  • Dimensionality reduction, including principal component analysis, multi-dimensional scaling, t-SNE.
  • Clustering, including hierarchical clustering, k-means, Gaussian mixture models
  • Hidden Markov models
  • (Deep) neural networks
  • Kernel-based methods, including support vector machines

After having followed this course, the student has a good understanding of a wide range of machine learning techniques and is able to recognize what method is most applicable to data analysis problems (s)he encounters in bioinformatics and systems biology applications.

Target audience

The course is aimed at PhD students with a background in bioinformatics, systems biology, computer science or a related field, and life sciences. Participants from the private sector are also welcome. A working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.

More information

Software used in the computer labs to install on your own computer will be made available before the start of the course.

For more information about the course you can contact Perry Moerland.


The registration fees for this 5-day course are:

  • PhD student: 400 euro (excl. VAT)
  • Academic researcher (PI/Postdoc): 600 euro (excl. VAT)
  • Industry: 900 euros (excl.VAT)

The course fee includes:

  • Course material
  • Catering: coffee, tea and lunch will be provided.

There is room for max. 16 participants. Registration is closed!

Find the general enrollment information here.

For past editions check the course archive

For an overview of upcoming BioSB courses click here