Andre Kahles: Katalogdaten im Herbstsemester 2022 |
| Name | Herr Dr. Andre Kahles |
| Adresse | Professur für Biomedizininformatik ETH Zürich, CAB F 52.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 90 67 |
| andre.kahles@inf.ethz.ch | |
| Departement | Informatik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 261-5112-00L | Algorithms and Data Structures for Population Scale Genomics  Number of participants limited to 30. | 3 KP | 2G | A. Kahles | |||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Research 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Over 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-1299-00L | Bioinformatics | 6 KP | 4G | S. Sunagawa, P. Beltrao, A. Blasimme, V. Boeva, A. Kahles, C. von Mering, N. Zamboni | |||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Students will study bioinformatic concepts in the areas of genomics, metagenomics, proteomics, biological networks, biostatistics and bioethics. Through integrated lectures, practical hands-on sessions and homework assignments, students will also be trained in analytical and programming skills to meet the emerging increase in data-driven knowledge generation in biology in the 21st century. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students will have an advanced understanding of the underlying concepts behind modern bioinformatic analyses at genome, metagenome and proteome-wide scales. They will be familiar with the most common data types, where to access them, and how to analytically work with them to address contemporary questions in the field of biology. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Course participants have already acquired basic programming skills in UNIX, Python and R. Students bring their own computer with keyboard, internet access (browser) and software to connect to the ETH network via VPN. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen |
| ||||||||||||||||||||||||||||||||||||||||||||||||||