Rudolf Pillich

The paper uses single-cell transcriptomics to analyze an IPF cohort, revealing a transcriptional deficit refractory to current treatments and identifying STAT3 as a key regulator in the therapeutic gap.

The paper evaluates 46 human interactomes to identify which are best for disease gene discovery and interaction prediction, finding large networks like HumanNet perform better for disease genes while smaller ones like DIP excel in prediction.

NDEx IQuery provides a tool for network-based gene set analysis using diverse pathways and integrates with existing resources to enhance pathway interpretation.

The paper identifies consensus transcriptional regulatory networks of coronavirus infections by analyzing differential gene expression across multiple CoV strains and validates key regulatory targets.