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Module Number:
| 13978
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| Module Title: | Bioinformatics: Artificial Intelligence and Algorithmic Approaches |
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Bioinformatik: Methoden aus Künstlicher Intelligenz und Algorithmik
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| Department: |
Faculty GW - Faculty of Health Sciences Brandenburg
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| Responsible Staff Member: | -
Prof. Dr. rer. nat. Schliep, Alexander
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
On special announcement
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| Credits: |
6
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| Learning Outcome: | After successfully completing the module, students will have acquired an introduction to modern bioinformatics and to selected applications from biology and medicine. They understand the methodology through presentation of the central computational problems and an introduction of solutions based on classical algorithms and statistical machine learning, as well as modern deep learning approaches. |
| Contents: | The focus will be on four fundamental problem areas:
- Comparing sequences: Sequence alignment algorithms, Genome-scale approaches using index data structures, Alignment-free methods
- Analyzing gene expression: alignment-based and alignment-free methods to analyzing RNASeq, single-cell analysis
- Signals in sequences: identification of motifs, accessibility, and modification of DNA
- Sequence variations and relation to phenotypes: structural variants in disease, pan-genome approaches
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| Recommended Prerequisites: | - Basic knowledge of probability and statistics, algorithms and data structures at the undergraduate level
- Introduction to machine learning at Master’s level
- Working knowledge of Python
- Knowledge of the contents of module 14336 Introduction to Bioinformatics
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Lecture
/ 2 Hours per Week per Semester
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Exercise
/ 2 Hours per Week per Semester
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Study project
/ 30 Hours
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Self organised studies
/ 90 Hours
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| Teaching Materials and Literature: | - Biological Sequence Analysis. Cambridge University Press (Exerpts)
- Genome-Scale Algorithm Design. Cambridge University Press (Exerpts)
- Review and Original Research Articles for the ML aspects
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| Module Examination: | Prerequisite + Final Module Examination (MAP) |
| Assessment Mode for Module Examination: | Prerequisite for Final Module Examination:- Successful completion of homework
Final Module Examination:
- Written examination, 120 min. OR
- Oral examination, 30-45 min. (with small number of participants)
In the first lecture it will be announced, the examination will be offered in written or oral form. |
| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | None |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
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Master (research-oriented) /
Mathematical Data Science /
PO 2025
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| | This module has been approved for the general studies. |
| Remarks: | None |
| Module Components: | - Lecture „Bioinformatics: AI and Algorithmic Approaches“
- Accompanying exercise
- Related examination
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| Components to be offered in the Current Semester: | |
