Thomas E. Bartlett

This paper improves gene-regulatory network inference by using epigenomic priors, showing better accuracy with DNA methylation data compared to chromatin accessibility data.

The paper evaluates gene regulatory network prediction methods in early human embryos, finding that MICA, combining mutual information with chromatin accessibility, provides more reproducible and biologically meaningful results.

The paper quantifies luminal progenitor and other epithelial cell types in breast tissue using DNA and RNA measurements, finding elevated levels in hormone receptor negative cancers and BRCA1 mutation carriers.

The paper introduces a dynamic genomic network model that integrates single-cell transcriptome and DNA-binding data to infer regulatory patterns in cortical development.