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26.01.2022 23:37:16
phy614 - Personalized Medicine (Complete module description)
Original version English Download as PDF
Module label Personalized Medicine
Module code phy614
Credit points 6.0 KP
Workload 180 h
180 h (Präsenzzeit 72h, Selbststudium: 108h)
Institute directory Institute of Physics
Applicability of the module
  • Master's Programme Engineering Physics (Master) > Schwerpunkt: Biomedical Physics
Responsible persons
Schmidt, Thorsten (Authorized examiners)
Skills to be acquired in this module
Students should understand current high-throughput methods used in research and clinics. They should be aware of the advantages and challenges and should be able to judge and interpret the results. In addition, the students should accomplish a sound understanding of basic algorithms which are used to analyze big and complex data sets. They should be able to choose, use and interpret appropriate tools and methods. Finally, students should be able to address the limitations and prospects of big-data analyses in complex systems.
Module contents
The lecture aims to provide an overview about current experimental high-throughput methods and bioinformatic algorithms to address the challenges of exponentially growing amounts of data. In addition to basic algorithms and methods like alignments, hidden markov models, Viterbi, graphs or protein-protein interaction networks, the lecture aims to gives an introduction to a data-driven view of disease biology
Reader's advisory

Genomic and Personalized Medicine:

V1-2 Huntington F. Willard, Geoffrey S. Ginsburg; Academic Press; 2. Edition. (30. Oktober 2012);

Cancer Genomics:

From Bench to Personalized Medicine; Graham Dellaire, Jason Berman; Academic Press; 1. Edition (17. January 2014);

Systems Biology:

A Textbook; Eda Klipp et al (2009); Wiley-VCH Verlag GmbH, Co. KGaA; Auflage: 1. Edition;
Language of instruction English
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Modullevel / module level EB (Ergänzungsbereich / Complementary)
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method Vorlesung: 4 SWS
Vorkenntnisse / Previous knowledge Statistics, Computing
Examination Time of examination Type of examination
Final exam of module
Course type Lecture
Frequency SoSe oder WiSe
Workload attendance 28 h