Das Herbstsemester 2020 findet in einer gemischten Form aus Online- und Präsenzunterricht statt.
Bitte lesen Sie die publizierten Informationen zu den einzelnen Lehrveranstaltungen genau.

Andre Kahles: Katalogdaten im Herbstsemester 2018

NameHerr Dr. Andre Kahles
Adresse
Professur für Biomedizininformatik
ETH Zürich, CAB F 52.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
E-Mailandre.kahles@inf.ethz.ch
DepartementInformatik
BeziehungDozent

NummerTitelECTSUmfangDozierende
261-5112-00LAdvanced Approaches for Population Scale Compressive Genomics
Number of participants limited to 30.
3 KP2GA. Kahles
KurzbeschreibungResearch in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions.
LernzielThis interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it.
InhaltOver the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content.
1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants.
2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures.
3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data.
551-1299-00LIntroduction to Bioinformatics Belegung eingeschränkt - Details anzeigen
Number of participants limited to 50.
6 KP4GS. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni
KurzbeschreibungThis course introduces principle concepts, the state-of-the-art and methods used in the field of Bioinformatics. Major topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming.
LernzielThe course will provide students with the theoretical background in the area of genomics, metagenomics, network bioinformatics and imaging. In addition, students will acquire basic skills in applying modern methods that are used in these sub-disciplines of Bioinformatics. Students will thus be able to access and analyze DNA sequence information, construct and interpret networks that emerge though interactions of e.g. genes/proteins, and extract information based on computer-assisted image data analysis. Students will also be able to assess the ethical implications of access to and generation of new and large amounts of information as they relate to the identifiability of a person and the ownership of data.
InhaltEthics
Case studies to learn about applying ethical principles in human genomics research

Genomics
Genetic variant calling
Analyze and critical evaluate genome wide association studies

Metagenomics
Reconstruction of microbial genomes
Microbial community compositional analysis
Quantitative metagenomics

Network bioinformatics
Inference of molecular networks
Use of networks for interpretation of (gen)omics data

Imaging
High throughput single cell imaging
Image segmentation
Automatic analysis of drug effects on single cell suspension (chemotyping)
Voraussetzungen / BesonderesBringing your own laptop is a prerequisite for taking this course.