Joost Wagenaar

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

The paper introduces a standardized, open-source protocol for noninvasive joint injury in mice, resulting in post-traumatic osteoarthritis, and provides tools for reproducibility and analysis.

This study identifies sensory fiber types in mouse TMJ tissues using immunohistochemistry, finding peptidergic fibers dominate with minimal non-peptidergic nociceptors.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This paper introduces UNITO, a deep learning framework for automated cytometric gating that matches human performance and provides interpretable results without requiring manual input.

The paper introduces cyMAE, a masked autoencoder for automated immunophenotyping, showing it improves accuracy and interpretability over manual methods in cytometry data analysis.

This paper introduces QOT, a method for efficiently computing sample-level distance matrices from single-cell omics data, showing improved accuracy and robustness over existing OT-based approaches.

The paper describes the development of a resource for the autonomic nervous system community, including databases, connectivity maps, and simulation tools.