Using Explainable AI to Uncover Gene Relevance in scRNA-seq Data

Single-cell RNA sequencing (scRNA-seq) provides detailed insights into gene expression at the cellular level. In this thesis, we use explainable AI (XAI) methods to investigate which genes most influence the predictions of a neural network trained on scRNA-seq data. Our goal is to compare these model-driven insights with established biological marker genes to better understand the relationship between machine learning decisions and biological knowledge.

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