phy614 - Personalized Medicine (Complete module description)

phy614 - Personalized Medicine (Complete module description)

Original version English PDF Download
Module label Personalized Medicine
Modulkürzel phy614
Credit points 6.0 KP
Workload 180 h
(
attendance: 56 hrs, self study: 124 hrs
)
Institute directory Institute of Physics
Verwendbarkeit des Moduls
  • Master's Programme Engineering Physics (Master) > Schwerpunkt: Biomedical Physics
Zuständige Personen
  • Schmidt, Thorsten (module responsibility)
  • Schmidt, Thorsten (Prüfungsberechtigt)
Prerequisites
Statistics, Computing
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
Literaturempfehlungen

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;
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Type of module Wahlpflicht / Elective
Module level EB (Ergänzungsbereich / Complementary)
Teaching/Learning method Lecture: 4 hrs/week
Previous knowledge Statistics, Computing
Examination Prüfungszeiten Type of examination
Final exam of module
KL
Form of instruction Lecture
SWS 2
Frequency SoSe oder WiSe
Workload Präsenzzeit 28 h