We mainly offer topics from the area of algorithmic bioinformatics. These can range from more theoretical algorithmic questions to workflow development for specific bioinformatics applications.
Please feel free to propose and discuss your own topic with us.
You find most referenced student theses here: https://www.cs.hhu.de/lehrstuehle-und-arbeitsgruppen/algorithmische-bioinformatik/lehre-und-abschlussarbeiten/abschlussarbeiten/abgeschlossene-arbeiten. Otherwise ask.
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.
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.