Florian Barkmann

This study uses sparse autoencoders to analyze hidden representations in single-cell foundation models, revealing interpretable biological and technical features and demonstrating their functional relevance.

CDState infers malignant cell subpopulations from bulk RNA-seq data, revealing gene programs and clinical associations in 33 cancer types.

This study benchmarks eight self-supervised learning methods on single-cell data across three tasks and eight datasets, finding SimCLR and VICReg to be the top performers.

CancerFoundation is a single-cell RNA-seq foundation model that outperforms existing models in batch integration and drug response prediction, even when trained on a smaller dataset.

The paper introduces ANS, a new gene signature scoring method for scRNA-seq, improving cell annotation by addressing limitations in existing methods and demonstrating strong classification performance.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

CanSig benchmarks methods for identifying transcriptional signatures in cancer using scRNA-seq data, finding BBKNN as a leading method with clinically relevant outcomes.