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55662-01 - Vorlesung: Applied Mathematics and Informatics in Drug Discovery 2 KP

Semester Herbstsemester 2024
Angebotsmuster Jedes Herbstsemester
Dozierende Jitao David Zhang (jitao-david.zhang@unibas.ch, BeurteilerIn)
Inhalt Applied mathematics and computer science are indispensable in modern drug discovery. They enable decisions that have direct impacts on lives. This introductory course offers a practitioner’s review of mathematical concepts, informatics tools, and industrial approaches in relevant fields, especially bioinformatics, molecular modelling, cheminformatics, mathematical modelling, experiment design and statistical inference, machine learning, and emerging deep-learning and generative approaches to artificial intelligence.

It is hoped that the students are exposed to the interdisciplinary and multiscale modelling nature of drug discovery. The ideal outcome is that the participants are motivated to deepen their knowledge in relevant fields in future study and practice in order to solve open challenges in drug discovery.
Lernziele We explore the drug-discovery process and study applications of mathematics and informatics with case studies. We examine how mathematics concepts and informatics tools are used to model complex systems at multiple levels - molecular level, cellular and omics level, organ- and system-level, and population level - and how the multiscale modelling approach contributes to drug discovery.
Literatur Lecture notes and slides. Recommend reading (papers, book chapters, etc.) and media (e.g. YouTube videos) will be distributed.
Weblink Check out the website at www.amidd.ch

 

Teilnahmebedingungen The course requires in-person attendence. Unfortunately, no remote or virtual attendance options are available.

Students of natrual sciences, including biology, physics, chemistry, pharmacy, and medical students are as much welcome as students of mathematics and computer sciences.

Though no prerequisite courses are obligatory, elementary understanding of statistics, probability, calculus, and ordinary differential equations are helpful. High-school knowledge in physics, chemistry, and biology are required. Knowledge and proficiency in at least one programming language (preferably C/C++, Java, R, Python, or Julia) is very helpful to try real-world problems.
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz
HörerInnen willkommen

 

Intervall Wochentag Zeit Raum
wöchentlich Freitag 12.15-14.00 Spiegelgasse 5, Seminarraum 05.002

Einzeltermine

Datum Zeit Raum
Freitag 20.09.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 27.09.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 04.10.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 11.10.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 18.10.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 25.10.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 01.11.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 08.11.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 15.11.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 22.11.2024 12.15-14.00 Uhr Biozentrum, Hörsaal U1.141
Freitag 29.11.2024 12.15-14.00 Uhr Dies Academicus
Freitag 06.12.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 13.12.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Freitag 20.12.2024 12.15-14.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Module Modul: Angewandte Mathematik (Bachelorstudium: Mathematik)
Modul: Applications and Related Topics (Bachelorstudium: Computer Science)
Modul: Applications and Related Topics (Bachelor Studienfach: Computer Science)
Modul: Electives in Data Science (Masterstudium: Data Science)
Leistungsüberprüfung Lehrveranst.-begleitend
Hinweise zur Leistungsüberprüfung Scores will be given in scale 1-6 by 0.5. The final note is given by participation including quizzes (30%), offline activities (40%), and a collaboration challenge in the final session (30%).
An-/Abmeldung zur Leistungsüberprüfung Anm.: Belegen Lehrveranstaltung; Abm.: stornieren
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,5
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch
Anbietende Organisationseinheit Fachbereich Mathematik

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