Paul T. Shannon

The paper constructs a genome-scale transcriptional regulatory network of the human placenta using genomic and transcriptomic data, identifying key regulators of preterm birth.

The paper develops a workflow to identify transcription factor binding sites across 27 human tissues using DNase-seq data, resulting in a comprehensive footprint atlas with high-quality predictions.

The paper quantifies transcription factor abundances during human erythropoiesis and reveals gene regulatory principles and imbalances in coactivator-corepressor ratios.

The paper uses network modeling to identify molecular mechanisms driving T cell exhaustion and validates predictions through experimental testing.

The paper presents tools for ensuring FAIRness in big biomedical data analysis through a case study on creating a transcription factor binding site atlas from ENCODE data.

The paper constructs a brain-specific transcriptional regulatory network model to identify core transcription factors involved in psychiatric and neurodegenerative diseases.