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 Python3 based on the widely applied scikit-learn 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 intended for PhD students, post-docs and staff scientists who are interested in applying ML to analyze omics data.

Learning objectives

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 scikit-learn Python framework
Interpret and visualize the results obtained from ML analyses

Prerequisites

Knowledge / competencies

No prior knowledge of ML concepts and methods is required.

Knowledge of different -omics data is recommended.

Familiarity with the Python programming language and pandas dataframes as well as a basic knowledge on statistics is required.

The competences and knowledge levels required correspond to those taught in courses such as: First Steps with Python in Life Sciences and Introduction to statistics with R. Test your skills with Python and statistics with the quiz here, before registering.

Technical

You will need to have a recent python3 as well as a number of python libraries installed. Please follow these instructions to setup your environment (note: these instructions use conda to manage the different packages)

Please perform these installations PRIOR to the course and contact us if you have any trouble.

Application

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

While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

Applications will close as soon as the places will be filled up, until 18/09/2025. Deadline for registration and free-of-charge cancellation is set is set to 18/09/2025. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

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.

Venue and Time

This course will take place at the Kollegienhaus of the University of Basel.

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

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

Additional information

Coordination: Grégoire Rossier, SIB Training Group

We will recommend 0.50 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.

Introduction to Machine Learning with Python