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| 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 |
| 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 (Bachelor Studienfach: Computer Science) Modul: Applications and Related Topics (Bachelorstudium: 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 |