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Jan Baumbach

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Open access Oct 2026

Integrating network medicine and foundation models reveals cell-type-specific regulatory alterations in Alzheimer’s disease

Abstract Alzheimer’s disease is a complex neurodegenerative disorder characterized by progressive cognitive decline and neuroinflammation. Although its molecular hallmarks are well documented, cell-type-specific mechanisms driving gene dysregulation remain elusive. While single-cell RNA sequencing resolves cellular sta...

Andrea Álvarez-Pérez, Alejandro Rodríguez González, Jan Baumbach et al. · 0 citations
Preprint Sep 2026

Quantum Optimisation for Protein-Protein Interaction Network Alignment

Protein-protein interaction (PPI) network alignment combines topological and sequence information to identify conserved modules across species, but global alignment remains challenging: heuristics sacrifice optimality, while exact methods lack scalability. We model the alignment as a weighted maximum common induced sub...

M. Stahl, Robert J. Banks, M. Traube et al. · 0 citations
Open access Jul 2026

Prediction of single cell expression from low-plex immunofluorescence images for guiding precision oncology

Spatial omics allows for comprehensive investigation of the tumor immune microenvironment (TIME). Stratifying patients by their TIMEs contributes with insights in tumor immune response and has the potential to guide treatment decision-making in the immuno-oncology setting. However, high-plex spatial omics approaches st...

Markus Heidrich, Daniel Nilsson, A. M. Frank et al. · 0 citations
#machine learning Preprint Aug 2026

EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders

This work proposes Explainable Probing of Cross-Domain Sparse Embeddings (EXPOSE), a framework that uses Sparse Autoencoders (SAEs) as an explainable bottleneck to identify and suppress domain-specific components in VFM embeddings.

Anja Witte, M. Lennartz, Jan Baumbach et al. · 0 citations

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