phy614 - Personalized Medicine
| Module label | Personalized Medicine |
| Modulkürzel | phy614 |
| Credit points | 6.0 KP |
| Workload | 180 h
( attendance: 56 hrs, self study: 124 hrs )
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| Institute directory | Institute of Physics |
| Verwendbarkeit des Moduls |
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| Zuständige Personen |
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| 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 |
| Examination | Prüfungszeiten | Type of examination |
|---|---|---|
| Final exam of module | KL |
| Lehrveranstaltungsform | Lecture |
| SWS | 2 |
| Frequency | SoSe oder WiSe |
| Workload Präsenzzeit | 28 h |
