Computational Approaches to Deciphering the Molecular and Cellular Heterogeneity of Alzheimer's Disease

Doctoral thesis (2025)

Authors

Gerard A. Bouland Pattern Recognition and Bioinformatics - EEMCS

Contributors

Marcel Reinders Pattern Recognition and Bioinformatics - EEMCS (promotor)
Ahmed Mahfouz Leiden University Medical Center (copromotor)
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2025

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

In summary, the contributions within this thesis advance Alzheimer's research by introducing new computational tools and methods to better understand the genetics of the disease and cellular mechanisms. Additionally, showing that single-cell gene expression can be effectively analyzed in a binary format (expressed or not) simplifies genomic data analysis, making it more accessible, efficient, and applicable to a range of diseases and conditions.

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