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Protein Prediction II for Computer Scientists (IN2291)

Lehrveranstaltung 0000001357 im WS 2016/7

Basisdaten

LV-Art Vorlesung-Übung
Umfang 6 SWS
betreuende Organisation Informatik 12 - Lehrstuhl für Bioinformatik (Prof. Rost)
Dozent(inn)en Burkhard Rost
Termine

Zuordnung zu Modulen

weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise This lecture has first been given to computational biologists. We are in the process of developing this module as a new lecture that is taught in parallel to that for computational biologists, and that requires much less prior knowledge and will focus more on algorithms than on the biological importance of methods. Intro: What is a protein? What is protein function? Overview over prediction of protein function. Predicting protein function using sequence: motifs, domain assignment, annotation transfer by homology, de novo predictions. In particular, prediction of: subcellular localization, protein-protein interactions, protein-DNA and –RNA interactions, protein-substrate interactions, protein networks, GeneOntology (GO), Enzyme Classification, prediction of enzymatic activity, prediction of functional classes (e.g. GO classes). Prediction of the effect of single point mutations (sequence variants) on protein function and the organism. Prediction of phenotype from genotype. As for the first part (Protein Prediction I), the lectures include an introduction into machine learning with particular focus on how to avoid over-estimating performance. The students learn how to write a prediction method starting from data in varying form (depending on the problem at hand). In some cases, they will get the complete input from the tutors, in others, they will have to write database parsers and generate the input/output data they will need during the labwork. Each team will thoroughly estimate the performance of the tool they created and the team will present their results to their peers and to the tutors.
Links E-Learning-Kurs (z. B. Moodle)
Aktuelle Informationen
TUMonline-Eintrag

Gleiche Lehrveranstaltungen (z. B. in anderen Semestern)

SemesterTitelDozent(en)Termine
WS 2023/4 Protein Prediction II for Computer Scientists (IN2291) Olenyi, T. Rost, B. Di, 14:00–16:00, MI 00.13.009A
Do, 12:00–14:00, MI 00.13.009A
Mo, 10:00–12:00, MI 01.09.034
sowie einzelne oder verschobene Termine
WS 2022/3 Protein Prediction II for Computer Scientists (IN2291) Erckert, K. Olenyi, T. Rost, B. Senoner, T. Do, 10:00–12:00, LMU-HS
Mo, 10:00–12:00, MI 01.09.034
Di, 10:00–12:00, LMU-HS
WS 2021/2 Protein Prediction II for Computer Scientists (IN2291) Rost, B. Do, 10:00–12:00, LMU-HS
Do, 12:00–14:00, virtuell
Di, 10:00–12:00, LMU-HS
WS 2020/1 Protein Prediction II for Computer Scientists (IN2291) Rost, B. Di, 10:00–12:00, virtuell
Do, 10:00–12:00, virtuell
Do, 12:00–14:00, virtuell
WS 2019/20 Protein Prediction II for Computer Scientists (IN2291) Rost, B.
WS 2018/9 Protein Prediction II for Computer Scientists (IN2291) Rost, B.
WS 2017/8 Protein Prediction II for Computer Scientists (IN2291) Rost, B.
WS 2015/6 Protein Prediction II for Computer Scientists (IN2291)
WS 2014/5 Protein Prediction II for Computer Scientists (IN2291)
WS 2013/4 Protein Prediction II for Computer Scientists (IN2291)
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