Deep Learning for Life Sciences - fundamentals and applications

Date 25 November 2021
Speaker(s) Markus Müller, Thuong Van Du Tran, Carlos-Andrés Peña-Reyes, Frédérique Lisacek, Panagiotis Papasaikas, Valentina Boeva, Karsten Borgwardt
Cancellation deadline 10 Nov 2021
City Streamed


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.


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


Knowledge / competencies

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


No technical prerequisites are required.

Schedule - CET time zone

9:00 - 12:30: Introduction to the theory of deep learning

12:30 - 13:30 Lunch break

13:30 - 16:30: Minisymposium

  • 13:30 - Carlos-Andrés Peña-Reyes (HEIG-VD & SIB) - Towards BacterioPhage Genetic Edition: Deep Learning Prediction of Phage-Bacterium Interactions
  • 14:00 - Frédérique Lisacek (UniGe & SIB) - Deep Learning prediction of glycan-protein interactions
  • 14:30 - Panagiotis Papasaikas (FMI & SIB) - Deep Generative Networks for single-cell transcriptomics

15:00 - 15:15 Coffee break

  • 15:15 - Valentina Boeva (ETHZurich & SIB) - Regularization for sparsity, biological priors and neural networks in cancer survival models
  • 15:45 - Karsten Borgwardt (ETHZurich & SIB) -Deep learning in systems biology: current challenges and future goals

16:15 - 17:00: Round table discussion


This course is free of charge.

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

Additional information

Coordination: Patricia Palagi

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

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