public:ba-themen
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
public:ba-themen [2023/07/13 09:05] – spohr | public:ba-themen [2025/02/04 09:30] (current) – spohr | ||
---|---|---|---|
Line 6: | Line 6: | ||
You find most referenced student theses here: https:// | You find most referenced student theses here: https:// | ||
+ | |||
=== BSc level === | === BSc level === | ||
+ | * develop HOG-based ILP to compute shortest common superstring of given k-mer set and also minimize the run length encoding of the mask. See https:// | ||
+ | * Using SAT to model first species counterpoint and compare to ILP implementation (Tanaka) Contact: Philipp | ||
+ | * Integrate/ | ||
+ | * Paper Dominik Heider https:// | ||
+ | * Maximizing diversity for anticlustering (while keeping optimal dispersion). Keywords: ILP, R, C++, maybe preprocessing, | ||
+ | * " | ||
+ | * 3D-time-dose-response surface fitting and extrapolation, | ||
+ | * Implementierung und Vergleichen von Metaheuristiken für Maximum Diversity bei Anticlustering [[https:// | ||
+ | |||
+ | === MSc level === | ||
+ | * Vehicle routing problem for leaf optimization. Get Martin' | ||
+ | * Data from Olga. compare molecular graphs. Contact: Gunnar | ||
+ | * Further stuff Nan (new stuff on image analysis) | ||
+ | |||
+ | === Other topics / No fixed level === | ||
+ | * Benchmarking and Testing of Clustering-based, | ||
+ | This project aims to evaluate different approaches for cell type prediction in high-plex imaging data, including clustering-based methods, manual annotation, and machine learning (ML) models. The study will compare the accuracy, reproducibility, | ||
+ | * Application and Optimisation of Automated Cell Type Prediction Tools for Distinct Hematologic and Solid Malignancies to Identify Distinct Changes in Patient Subgroups (Quantitative Biology M.Sc. project) | ||
+ | This project focuses on optimizing automated cell type prediction tools for specific hematologic and solid malignancies in multiplexed immunofluorescence imaging. By applying advanced computational methods taking into account the known composition and characterisation of the disease entity, the study will identify unique cellular alterations within specific patient subgroups, potentially revealing novel biomarkers or therapeutic targets. The work involves testing and refining prediction models to improve their accuracy in detecting disease-specific cellular patterns, contributing to precision oncology research. While this project focuses on the computational side of multiplexed immunofluorescence, | ||
+ | * Efficient heuristic or exact algorithms for circular sequence comparison in sub-quadratic time: Aligning bacterial genomes vs one another or reads of a circular genome vs a circular genome | ||
+ | * SWGTS-Expansions: | ||
* An edge-based ILP for modeling leaf venation patterns (builds on BSc thesis Mario Surlemont). Contact: Gunnar Klau | * An edge-based ILP for modeling leaf venation patterns (builds on BSc thesis Mario Surlemont). Contact: Gunnar Klau | ||
* SAT Formulation for MERIDA [[https:// | * SAT Formulation for MERIDA [[https:// | ||
* ILP for Matrix Reordering Problem [[https:// | * ILP for Matrix Reordering Problem [[https:// | ||
- | * Implementing a bi-clustering ILP and application to genomic data. Overview: overview: https:// | ||
* Implement an algorithm to find active modules (average Heinz) by maximizing a fractional objective function | * Implement an algorithm to find active modules (average Heinz) by maximizing a fractional objective function | ||
- | | + | * Spa-Typing and Flight Data: Collect Flight Data and Samples from Spa Database and look for Correlations (Check if a high volume of flights correlates to similar detected Spa-Types / Develop a framework to check correlation between flight volume and genetic information), Build on Ninas work, Contact: Philipp |
- | * Maximize dispersion for anticlustering using constraint programming. Builds on BSc thesis Max Diekhoff. R skills necessary/ | + | |
- | | + | |
- | * Cytoscape-Fun: | + | |
- | * Overlap epitope vaccine design: Reformulate and re-implement the ILP proposed by Dorigatti and Schubert in https:// | + | |
- | * Projektarbeit, | + | |
* Backport Sven's 0-Edge-CE-Heuristic into Yoshiko Main (and thus Cytoscape App), Contact: Sven, (Philipp?) | * Backport Sven's 0-Edge-CE-Heuristic into Yoshiko Main (and thus Cytoscape App), Contact: Sven, (Philipp?) | ||
+ | * Projektarbeit, | ||
- | * Using SAT to model first species counterpoint and compare to ILP implementation (Tanaka) Contact: Philipp | ||
- | === MSc level === | ||
- | * Implement an ILP-based approach to enumerate common molecular fragments. Background: https:// |
public/ba-themen.1689239105.txt.gz · Last modified: 2023/07/13 09:05 by spohr