4EU+ Deep Learning for Life Sciences (DeepLife) - MB151P143

• Title: 4EU+ Deep Learning for Life Sciences (DeepLife)
• Czech title: Hluboké učení pro přírodní vědy
• Guaranteed by: Department of Cell Biology (31-151)
• Faculty: Faculty of Science
• Actual: from 2023
• Semester: summer
• E-Credits: 5
• Examination process: summer s.:
• Hours per week, examination: summer s.:2/2, Ex [HT]
• Capacity: 20
• Min. number of students: unlimited
• 4EU+: yes
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Explanation: dodatečně založeno 15.2.24
• Note: enabled for web enrollment
• Guarantor: Mgr. Marian Novotný, Ph.D.
• Teacher(s): doc. RNDr. David Hoksza, Ph.D.; Mgr. Marian Novotný, Ph.D.; Ing. Martin Schätz, Ph.D.; Mgr. Vít Škrhák

Annotation

Project DeepLife intends to be a comprehensive and application-oriented teaching project which will introduce students to the most advanced algorithms and applications of deep learning in life sciences. For this, we will build on existing complementarities between the bioinformatics master programs of the five universities identified during the first educational project, and offer a new and comprehensive course covering the different aspects of Deep learning approaches in life sciences. We will set a strong focus on complementary application areas in life sciences and practical implementations. We will cover three very active 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 hybrid mode, with online lectures by teachers from different institutions and on-site practical exercises in the form of Q&A sessions in smaller groups. Our objective is to bring together the strong and complementary expertise in structural bioinformatics (Paris, Prag, Warsaw), single-cell genomics (Heidelberg, Warsaw) and image analysis (Heidelberg, Prag, Milano).

The course is composed of online lectures and online exercises in the form of notebooks that are students supposed to succesfully complete.

The course will be extended for best 15 students with an on-site 2-day hackathon during which mixed teams will work on small implementation projects around selected topics of deep-learning.

Last update: Novotný Marian, Mgr., Ph.D. (16.02.2024)

Syllabus

Sylabus:

Date	Title	Speaker
26.02	Intro and Mathematical foundation to DL	Bartek Wilczynski (Warsaw)
04.03	Convolutional and Recurrent neural networks	Marco Frasca (Milano)
11.03	Autoencoders and variational autoencoders	Carl Herrmann (Heidelberg)
18.03	Attention mechanisms and transformers	Dario Malchiodi (Milano)
08.04	Transformers and RNN for sequence analysis	Dario Malchiodi (Milano)
15.04	Models for multimodal data integration	Britta Velten (Heidelberg)
22.04	VAE in single-cell genomics	Carl Herrmann (Heidelberg)
29.04	AF, EMSFold to predict structure of proteins	Joanna Sulkowska (Warsaw)
06.05	RNN, CNN models for topology/graph analysis in biopolymers	Joanna Sulkowska (Warsaw)
13.05	Deep learning models for protein-ligand binding site prediction	David Hoksza (Prague)
23.05	Diffusion models for protein design	Elodie Laine (Paris)
27.05	Intro to BioImage Analysis and Deep Learning Utilization	Martin Schatz (Prag)
03.06	Deep Architectures for sampling macromolecules	Grégoire Sergeant-Perthuis (Paris)
10.06	Deep learning for segmentation	Karl Rohr (Heidelberg)

 

 

Last update: Novotný Marian, Mgr., Ph.D. (16.02.2024)