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Deep learning in life science

General data

Course ID:	1000-2M23DLS
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Deep learning in life science
Name in Polish:	Deep learning in life science
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science Elective courses for Machine Learning Specific programme courses of 2nd stage Bioinformatics
ECTS credit allocation (and other scores):	6.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO WYK TU W TH LAB LAB FR
Type of class:	Lab, 30 hours more information Lecture, 30 hours more information
Coordinators:	Bartosz Wilczyński
Group instructors:	Marcin Możejko, Maciej Sikora, Joanna Sułkowska, Bartosz Wilczyński
Course homepage:	https://deeplife4eu.github.io/
Students list:	(inaccessible to you)
Examination:	Examination
Main fields of studies for MISMaP:	biology computer science
Prerequisites:	Introduction to computer science 1000-711WIN Statistical data analysis 1000-714SAD
Course dedicated to a programme:	4EU+Courses
Short description:	As part of the Seed4EU+ action within the 4EU+ alliance of European universities, Heidelberg University will coordinate a joint course on Applications of Deep Learning in Life Sciences (DeepLife) involving the universities of Paris-Sorbonne, Warsaw, Prague, Milano and Heidelberg. The purpose of this course is to give an insight in the very active field of DL in the field of biomedicine and highlight recent examples of applications in three distinct areas: single-cell genomics protein bioinformatics biomedical image analysis.
Full description:	Introduction: 1. Intro and Mathematical foundation to DL 2. Convolutional and Recurrent neural networks 3. Autoencoders and variational autoencoders 4. Attention mechanisms and transformers Genomics: 5. Transformers and RNN for sequence analysis 6. Models for multimodal data integration 7. VAE in single-cell genomics Protein Structure: 8. AlphaFold, EMSFold to predict structure of proteins 9. RNN, CNN models for topology/graph analysis in biopolymers 10. Deep learning models for protein-ligand binding site prediction 11. Diffusion models for protein design BioImage analysis: 12.Intro to BioImage Analysis and Deep Learning Utilization 13.Deep Architectures for sampling macromolecules 14. Deep learning for segmentation
Bibliography:	Deep Learning book by Goodfellow, Bengio, Courville The Elements of Statistical Learning by Hastie, Tibshirani, Friedman An Introduction to Statistical Learning by Hastie, Tibshirani, Friedman (a simpler version of the previous book) Machine learning with PyTorch and scikit-learn by Raschka, Liu, Mirjalili (a great introduction into the technical aspects of DL in pyTorch).
Notes:	The credits for the course are granted based on homeworks done throughout the semester. The best students will be able to go in June to the hackathon in Heidelberg.

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