Inhalt
[ 993TALSALSV20 ] VL Artificial Intelligence in Life Sciences
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| Workload |
Education level |
Study areas |
Responsible person |
Hours per week |
Coordinating university |
| 1,5 ECTS |
M1 - Master's programme 1. year |
(*)Artificial Intelligence |
Günter Klambauer |
1 hpw |
Johannes Kepler University Linz |
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| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2021W |
| Objectives |
This course presents artificial intelligence methods in Life Sciences, concretely in drug discovery, pharmacology, toxicology and bioinformatics. The course will demonstrate the application and use of machine learning in Life Sciences, with the focus on the field of small molecules and drug design. Both theoretical foundations of methods as well as basics of the implementation of those will be given.
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| Subject |
- Basic machine learning (ML) methods in Life Sciences
- Molecular representations
- QSAR, chemoinformatics, virtual screening, bioassays
- ML methods for drug target, activity and toxicity prediction
- Generative models for molecules
- Models for chemical reactions
- Deep neural networks for genomic and transcriptomic data
- Interpretability
- Machine learning applications for biological images
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| Criteria for evaluation |
Exam, assignments
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| Methods |
Presentations, blackboard, code examples, discussion
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| Language |
English |
| Study material |
Lecture notes, presentation slides, code
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| Changing subject? |
No |
| Corresponding lecture |
in collaboration with 993TALSALSU20: UE Artificial Intelligence in Life Sciences (1.5 ECTS) equivalent to
993TALSALSK19: KV Artificial Intelligence in Life Sciences (3 ECTS)
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| On-site course |
| Maximum number of participants |
- |
| Assignment procedure |
Direct assignment |
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