Nicholas De Veaux

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

The paper develops methods to infer transcriptional regulatory networks in Th17 cells using ATAC-seq and RNA-seq data, revealing new roles for transcription factors and their modules.

This study improves gene regulatory network inference by integrating multiple datasets using multitask learning, showing better network reconstruction and robustness to dataset heterogeneity.