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DyHealthNet Projekt
Thema der Arbeit Systematic evaluation of approximative genome-wide association studies for dynamic phenotypic contexts
Beschreibung The DyHealthNet project seeks to create a network medicine platform to analyze population-based study data, like the CHRIS study, integrating diverse molecular, clinical, and lifestyle information. This approach aims to identify connections between genetic profiles, phenotypes, and key lifestyle factors (e.g., BMI, sex, age) in various contexts. Utilizing a dynamic graph model, the project differentiates between static and on-demand interactions to explore associations and disease modules relevant to specific contexts. Due to the extensive data volume, including millions of genetic variants, the project will develop advanced data models and indexing methods for efficient, context-sensitive analysis, facilitating rapid computations within a user-friendly web tool. The goal of this master thesis is to implement an approximation strategy using linkage disequilibrium blocks to speed up the calculation of genome-wide association studies in a specific user-defined context and benchmark the error rates of this approximation approach.
Anforderungen
  • Experience with Python programming
  • Basic knowledge of algorithmics and binary classifiers
  • Enthusiasm for systematic problem-solving
Betreuer(-in) Lis Arend (TUM) + Fabian Woller (FAU)
Kontakt lis.arend@tum.de

 

Bei Interesse an einer Abschlussarbeit in einem anderen Projekt als dem ausgeschriebenen, senden Sie bitte eine E-Mail an Prof. Dr. Markus List (markus.list@tum.de).