Protein Prediction II for Computer Scientists (IN2291)
Course 0000001357 in WS 2016/7
General Data
| Course Type | Lecture w/ Exercise |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 12 - Chair of Bioinformatics (Prof. Rost) |
| Lecturers |
Burkhard Rost |
| Dates |
Assignment to Modules
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IN2291: Protein Prediction II for Computer Scientists / Protein Prediction II for Computer Scientists
This module is included in the following catalogs:- Catalogue of non-physics elective courses
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 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. |
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| Links |
E-Learning course (e. g. Moodle) Current information TUMonline entry |