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--- public:ba-themen [2024/04/29 10:33] – tran
+++ public:ba-themen [2025/02/04 09:30] (current) – spohr
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 === 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://www.biorxiv.org/content/10.1101/2023.02.01.526717v1.abstract  Problem: not all SCSs possible with HOG? miss important ones? Contact: Gunnar, Sara?
-  * Cytoscape-Fun: Update some existing app and add feature or transpose some work we have into Cytospace e.g. leaf vein thingy, update maybe: https://github.com/ls-cwi/heinz-app, Contact: Philipp
   * Using SAT to model first species counterpoint and compare to ILP implementation (Tanaka) Contact: Philipp
   * Integrate/Implement CP ILP Formulations as a stand-alone application that processes MIDI or as a plugin for the scorewrite software MuseScore (Tanaka) Contact: Philipp
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   * Vehicle routing problem for leaf optimization. Get Martin's code to run. Produce better pictures for Daan. Make modifications to speed up. Check if the model is good enough, adapt, ... Contact: Gunnar
   * Data from Olga. compare molecular graphs. Contact: Gunnar
-  * Max subdag, indeg `<=` 1, Problem from Alex --> Arne Kugel??
   * Further stuff Nan (new stuff on image analysis)
-=== Other topics ===
+=== Other topics / No fixed level ===
+  * Benchmarking and Testing of Clustering-based, Manual, and ML-based Cell Type Prediction in High-Plex Imaging Data (Bioinformatic M.Sc. project)
+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, and computational efficiency of these techniques across diverse datasets. The findings will provide insights into the optimal strategies for cell identification in spatial omics studies, supporting more robust downstream biological interpretations.
+  * 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, interested students can also get hands-on experience in data generation and wet-lab techniques.
+  * 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: 1.) Add encrypted package transfer and key sharing for increased throughput/speed, 2.) Alternative Filtering Methods e.g. k-mer based filtering 3.) Metadata Managment System 4.) Division of long reads into segments ;  Skills: Docker-Compose, REST, Encryption, Client-Server Architecture
   * An edge-based ILP for modeling leaf venation patterns (builds on BSc thesis Mario Surlemont). Contact: Gunnar Klau
   * SAT Formulation for MERIDA [[https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab546/6342406]]. Contact: Nguyen Khoa Tran