Universität Wien FIND

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323103 VU Data science approaches for advancing drug discovery - MPS5 (2021S)

2.00 ECTS (1.00 SWS), SPL 32 - Pharmazie
Prüfungsimmanente Lehrveranstaltung

Details

Sprache: Englisch

Lehrende

Termine

Introductory Meeting ("Vorbesprechung"):
March 2, 2021, 9:00-9:30 am, online meeting (link to the online meeting will be provided via moodle)


Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Data science has become an important field in drug discovery and development nowadays. It offers a level of understanding of health, disease and treatment on a scale never before imagined, i.e. it can help researchers to find new drugs or re-use old drugs for new indications.

In this lecture and seminar we will talk about existing data sources in the open domain, database schemes/structures, database managment systems, data querying, statistical learning, machine learning, chemical data representations used in the domain of life sciences. We will further introduce you into the data management/manipulation tool KNIME, and give some basic introduction into SQL and jupyter notebooks (python).

Content of the lectures:

- VO 1: Traditional drug discovery paradigms and rational drug discovery
- VO 2: Introduction to data-driven drug discovery and translational medicine
- VO 3: Statistics for data sciences: distributions, correlations, hypothesis testing
- VO 4: Statistical Learning/Machine Learning Part 1
- VO 5: Statistical Learning/Machine Learning Part 2
- VO 6: Chemical Data: chemical structure representations, chemical descriptors
- VO 7: Data sources, data integration
- VO 8: Database schemes/structures, data querying

Data Science put into praxis:

The practical part will be performed in the form of either a Journal Club presentation or a small data science project conducted by each student; in the end every student will give a final presentation

Art der Leistungskontrolle und erlaubte Hilfsmittel

There will be *no* written exam this semester (due to the COVID-19 pandemics and resulting requirement of distant learning). The assessment will be based on the active contribution during the lectures and on basis of the final presentations and/or the data science project (performed by each student).

Mindestanforderungen und Beurteilungsmaßstab

Active contribution during the Practical part;
Presence at the 8 lectures;
Journal Club presentation or Executed Data Science Project at the end of the course;
material for further reading will be pointed to in each individual lecture

Prüfungsstoff

Content of the lectures; material for further reading; Journal Club papers; Data Science Project;

Literatur


Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 10.02.2021 11:49