Deep learning models are increasingly applied to life sciences applications, in the field of genomic data, protein design and image analysis. Hence, it is crucial for students in the field of computational biology to learn about foundations and novel development in this field. The goal of the project DeepLife is to build on existing complementarities between the bioinformatics master programs at partner universities, and to offer a new and comprehensive course covering the different aspects of Deep learning approaches in life sciences, with a strong focus on application areas and practical implementations. The course covers three application areas of deep learning in life sciences: (1) structural bioinformatics, (2) application of deep-learning to single-cell genomics, (3) biomedical image analysis. This course will be held in a hybrid mode, with (1) online lectures by teachers from the different institutions and (2) on-site practical exercises. Our objective is to bring together the strong and complementary expertise in structural bioinformatics (Paris, Prag), single-cell genomics (Heidelberg, Warsaw) and image analysis (Heidelberg, Prag). The course will end with an on-site 2-day hackathon during which mixed teams will work on small implementation projects around selected topics of deep-learning. Following the Meet-EU course in the first period, this course will strengthen even more the connections between the teams involved at the different sites and be a step forward towards a multi-site master program in Computational Biology within a Erasmus Mundus funding scheme.
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