This course is now full with a waiting list but will be given again on 4 - 5 October 2021.

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 these 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 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, First Steps in Statistics for Life Sciences and Introduction to statistics. Test your skills with Python and statistics with the quiz here, before registering.

Technical

This course will be streamed, you are thus required to have your own computer with an internet connection and the following tools installed PRIOR to the course:

• latest Python 3 distribution, preferably bundled using conda
• Jupyter
• the scipy library (NB: if you installed conda, then this library is already installed)
• scikitLearn

Although not mandatory, we also highly recommend you to use the same computer to connect to the zoom classroom and perform the exercises, otherwise we will have difficulties helping you debug your code.

Application

The registration fees for academics are 120 CHF and 600 CHF for for-profit companies. This includes course content material.

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.

Deadline for registration and free-of-charge cancellation is set is set to 24/05/2021. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

Venue and Time

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

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

Additional information

Coordination: Monique Zahn, 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.

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.