Anna C. Aschenbrenner

This study uses multi-omics clustering to identify three immunological endotypes in HIV patients, linking them to distinct HIV reservoir sizes and immune mechanisms.

The paper develops an in vitro platform to screen immune senomodulators using human-derived immune cells, enabling personalized anti-aging treatments through multi-scale molecular analysis.

The paper introduces cyCONDOR, a computational framework for high-dimensional cytometry data analysis, offering scalable and user-friendly tools for preprocessing, clustering, and downstream biological interpretation.

The paper characterizes human CMV-specific CD8+ T cells using a five-layer multi-omics approach and identifies novel T cell receptor sequences for CMV epitopes.

This paper integrates transcriptome and chromatin state data to develop a scalable pipeline for drug discovery, predicting cancer cell line sensitivity and guiding compound development.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

The paper generates synthetic embryo-like structures from ESCs through transcription factor-mediated reprogramming, showing implantation and decidual tissue formation.

This study uses single-cell RNA-seq and computational methods to analyze macrophage states in COPD patients, revealing transcriptional changes and metabolic shifts associated with the disease.

This study investigates how IL4 signalling and NCOR2 regulate the reprogramming of human monocytes into dendritic cells and macrophages, revealing distinct transcriptional and functional profiles based on cytokine exposure timing.