Overview

With the rise of new technologies, the volume of omics data in the fields of biology and medicine has grown exponentially in recent times and a major issue is to mine useful predictive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data in order to assist humans to deal with the large volume of multidimensional data. The analysis of such data is not trivial and ML is a necessary tool to extract knowledge and make predictions that can advance the field of bioinformatics.

This 2-day course will introduce participants to common ML algorithms and teach how to apply them to omics data in extensive practical sessions. The practical sessions will be conducted in R based on the tidymodels ML framework. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML methods and processes, as well as the practical skills in applying them to real world problems using publicly available biological or medical data sets.

Audience

This course is designed for PhD students, postdoctoral and other researchers in the life sciences from both academia and industry who are interested in applying ML to analyse their data, omics or otherwise.

Learning outcomes

At the end of the course, the participants are expected to:
* Understand the ML taxonomy and the commonly used machine learning algorithms for analysing “omics” data
* Understand differences between ML approaches and in which situations they can be applied
* Understand and critically evaluate applications of ML in omics studies
* Learn how to implement common ML algorithms using the tidymodels framework
* Interpret and visualize the results obtained from ML analyses

Prerequisites

Knowledge / competencies

Familiarity with the R programming language is required for this course, as well as some basic knowledge on statistics. Knowledge of the tidyverse, dplyr syntax, and ggplot plotting is also recommended. Knowledge of different omics data is also recommended.

As such, you should meet the learning outcomes of First Steps with R in Life Sciences and Introduction to statistics and Data Visualisation with R.

Technical

A Wi-Fi enabled laptop with latest R and RStudio versions installed, as well as a set of libraries which will be communicated prior to the course. There will be access to the eduroam and guest WiFi network.

Schedule - CET time zone

On both days the course will start at 9:00 and end around 17:00.

The first day will be dedicated to introducing the data preprocessing and exploration as well as unsupervised learning (Dimensionality Reduction, clustering) while the second day will cover in more depth the topic of supervised learning (classification, regression, cross-validation,...).

Application

The registration fees for academics are 200 CHF and 1000 CHF for for-profit companies.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close on 29/10/2026. Deadline for free-of-charge cancellation is set to 29/10/2026. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will take place at the University of Basel.

The course will start at 9:00 CET and end around 17:00 CET.

Precise information will be provided to the registered participants in due time.

Additional information

Coordination: Valeria Di Cola, SIB Training Group.

Helper: Joana Carlevaro.

We will recommend 0.5 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.