Marco Stock

The paper shows that sampling biases in negative regulatory interactions inflate performance in gene regulatory network inference and proposes a degree-aware sampling approach to address this issue.

This study shows that reprogramming success varies by cell type and is influenced by the expression of genes from the original cell type, with some cells resisting reprogramming and causing developmental issues.

geneRNIB provides a benchmark for gene regulatory network inference, showing simpler models often outperform complex ones in perturbation-informed metrics.

This study identifies H3K27ac as an epigenetic barrier to reprogramming-induced cell fate changes using an AI-based approach and shows that reducing H3K27ac improves reprogramming efficiency.

This study shows that H3K4 methylation acts as an epigenetic memory to support zygotic genome activation and embryonic development in Xenopus laevis.

The paper benchmarks algorithms for inferring gene regulatory networks from single-cell RNA-seq data by evaluating their ability to capture topological properties and identify hubs.

CIARA identifies markers of rare cell types from single-cell RNA-seq data, outperforming existing methods and discovering new cell populations in mouse and human samples.