Studienhandbuch | UE Artificial Intelligence in Life Sciences

Inhalt

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

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.
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
Criteria for evaluation	Assignments
Methods	Presentations, blackboard, code examples, discussoin
Language	English
Study material	Lecture notes, presentation slides, code
Changing subject?	No
Corresponding lecture	in collaboration with 993TALSALSV20: VL Artificial Intelligence in Life Sciences (1.5 ECTS) equivalent to 993TALSALSK19: KV Artificial Intelligence in Life Sciences (3 ECTS)

On-site course
Maximum number of participants	35
Assignment procedure	Direct assignment