Jake Hughey

Principal Investigator
Google Scholar

I’m an Assistant Professor of Biomedical Informatics and Biological Sciences at Vanderbilt.

My goal is to do excellent science, to communicate it clearly, and to help my mentees do the same. That’s hard. Fortunately, I’ve been surrounded by great scientists, communicators, and mentors at every stage of my career. I even have one at home (pilot).

As an undergraduate, I studied Biomedical Engineering and Mathematics. I also did research in the lab of John Wikswo, developing microfluidic devices to study signaling dynamics in single immune cells.

I then went out to the Bay Area and worked with Markus Covert, where I applied a combination of live-cell imaging and mathematical modeling to understand the dynamics of innate immune signaling. They eventually gave me a PhD in Bioengineering.

I then did a postdoc with Atul Butte, where I learned the wonder of applying computational methods to publicly available data to address basic biological questions and clinical needs. The learning process continues to this day. During my postdoc is when I began to focus on using computation to study circadian rhythms.


Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record, Hughey et al., bioRxiv

Simphony: simulating large-scale, rhythmic data, Singer et al., bioRxiv

LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data, Singer and Hughey, J Biol Rhythms 2018

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level, Gutschow et al., Mol Biol Cell 2018

Population-level rhythms in human skin with implications for circadian medicine, Wu et al., PNAS 2018

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors, Rhoades et al., Chronobiol Int 2018

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data, Hu et al., Cell Rep 2018

Tau-independent Phase Analysis: A Novel Method for Accurately Determining Phase Shifts, Tackenberg et al., J Biol Rhythms 2018

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns, Bastarache et al., Science 2018

Evidence for widespread dysregulation of circadian clock progression in human cancer, Shilts et al., PeerJ 2018

Live-cell measurements of kinase activity in single cells using translocation reporters, Kudo et al., Nat Protoc 2018

Self-reported dietary adherence, disease-specific symptoms, and quality of life are associated with healthcare provider follow-up in celiac disease, Hughey et al., BMC Gastroenterol 2017

Guidelines for Genome-Scale Analysis of Biological Rhythms, Hughes et al., J Biol Rhythms 2017

Machine learning identifies a compact gene set for monitoring the circadian clock in human blood, Hughey, Genome Med 2017

Differential phasing between circadian clocks in the brain and peripheral organs in humans, Hughey and Butte, J Biol Rhythms 2016

Is the Crowd Better as an Assistant or a Replacement in Ontology Engineering? An Exploration Through the Lens of the Gene Ontology, Mortensen et al., J Biomed Inform 2016

ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system, Hughey et al., Nucleic Acids Res 2016

Robust meta-analysis of gene expression using the elastic net, Hughey and Butte, Nucleic Acids Res 2015

Single-cell variation leads to population invariance in NF-κB signaling dynamics, Hughey & Gutschow et al., Mol Biol Cell 2015

High-sensitivity measurements of multiple kinase activities in live single cells, Regot et al., Cell 2014

The microfluidic multi-trap nanophysiometer for hematologic cancer cell characterization reveals temporal sensitivity of the calcein-AM efflux assay, Byrd et al., Sci Rep 2014

Single-cell and Population NF-κB Dynamic Responses Depend on Lipopolysaccharide Preparation, Gutschow & Hughey et al., PLOS One 2013

Single-cell NF-κB dynamics reveal digital activation and analog information processing, Tay & Hughey et al., Nature 2010

Computational modeling of mammalian signaling networks, Hughey et al., Wiley Interdiscip Rev Syst Biol Med 2010

A Noisy Paracrine Signal Determines the Cellular NF-κB Response to Lipopolysaccharide, Lee et al., Sci Signal 2009

Microfluidic platform for real-time signaling analysis of multiple single T cells in parallel, Faley et al., Lab Chip 2008