Deep Learning for Life Sciences - fundamentals and applications

This course is free of charge. All those who have registered before the Application Deadline will receive the information to connect the day before the course starts.

Overview

The aim of this course is to familiarise the participants with the deep learning model and some of its applications in life sciences. 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, and deep learning is one of these methods. Deep learning is based on artificial neural networks inspired from the structure and function of the human brain and has been widely applied in computer vision, natural language processing, computational biology, etc.

This course will be composed of a half-day introduction to the theory of deep learning and how it is related to machine learning and neural networks, and a half-day minisymposium consisting of short presentations by SIB researchers on the applications of deep learning. The minisymposium will be followed by a panel discussion between speakers and the audience, allowing the opportunity to debate on the advantages and pitfalls of these technologies for research projects.

Audience

This course is addressed to PhD students, post-docs and researchers in life sciences who would like to have a grasp of Deep Learning and how it can be applied to life sciences research.

Learning outcomes

At the end of the course, the participants will be able to:

• Discuss the deep learning model
• Identify deep learning parameters
• Distinguish applications of deep learning in life sciences

Prerequisites

Knowledge / competencies

Prior knowledge of ML concepts and methods is required, and familiarity with different omics data technologies is highly recommended.

Technical

No technical prerequisites are required.

Schedule - CET time zone

9:00 - 12:30: Markus Müller and Van Du Tran (Vital-IT, SIB) - Introduction to the theory of deep learning

12:30 - 13:30 Lunch break

13:30 - 17:00: Minisymposium

• 13:30 - Raphaelle Luisier (IDIAP Research Institute & SIB) - Decoding neurodegenerative disorders combining AI and high content imaging
• 14:15 - Daniele Silvestro (UniFribourg & SIB) - Supervised and reinforcement learning to measure biodiversity and guide conservation action
• 15:00 - Coffee break
• 15:20 - Joana Pereira (BioZentrum & SIB) - Leveraging the deep learning revolution to study the diversity of the catalogued protein universe
• 16:05 - Markus A Lill (University of Basel and SIB) - Physics-guided deep learning for the prediction of protein-ligand interactions
• 16:50 - 17:00: Questions and Answers

Application

This course is free of charge. All those who have registered before the Application Deadline will receive the information to connect the day before the course starts.

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

You will be informed by email of your registration confirmation.

Venue and Time

This course will be streamed.

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

All those who have registered before the Application Deadline will receive the information to connect the day before the course starts.

Additional information

Coordination: Patricia Palagi

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.