Stefanie Warnat‐Herresthal

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.

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.

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 paper demonstrates that memory-driven computing significantly accelerates NGS data processing and reduces energy consumption for RNA-seq tasks.

The paper develops robust and accurate classifiers for AML diagnosis using blood gene expression data and machine learning across 12,029 samples from 105 studies.