Pierre Bushel

Adjunct Lecturer on Biostatistics

Department of Biostatistics

Other Affiliation

Staff Scientist, Biostatistics and Computational Biology Branch, National Institutes of Health/National Institute of Environmental Health Sciences, RTP, NC


Ph.D., Bioinformatics, 2005, North Carolina State University
M.S., Molecular and Cellular Biology, 1989, Long Island University-Brooklyn campus
B.S., Zoology, 1984, University of Massachusetts-Amherst campus


Dr. Bushel’s research focuses primarily on predictive toxicogenomics, underlying regulatory mechanisms of transcriptional networks, cancer genomics and gene biomarker discovery. He and his collaborators use bioinformatics, computational biology and machine learning to address environmental and toxicologic concerns pertaining to public health. In particular, he designs and implements analytical methodologies for integrative analyses of genomics, genetics, epigenetic and other big data types. In addition, he develops software and databases for toxico-environmental health informatics and deep learning for massive analysis of gene expression data.

Acetaminophen toxicity, hepatocellular carcinoma, anticancer therapeutic drug combinations, interactions between genomes, mode of action crosstalk, single-cell mRNA data clustering and analytics for reproducible science in big data through the U.S. FDA-led Massive Analysis and Quality Control (MAQC)\SEquence Quality Control (SEQC) consortiums are current areas of interest.

PubMed Publications

Selected Publications

Davis M, Knight E, Eldridge SR, Li J, Bushel PR. Transcriptomic profiles of tissues from rats treated with anticancer drug combinations. Sci Data. 2019 Jan 8;6:180306.

Bushel PR, Paules RS, Auerbach SS. A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples. Front Genet. 2018 Oct 30;9:485

Funderburk KM, Auerbach SS, Bushel PR. Crosstalk between Receptor and Non-receptor Mediated Chemical Modes of Action in Rat Livers Converges through a Dysregulated Gene Expression Network at Tumor Suppressor Tp53. Front Genet. 2017 Oct 24;8:157.

Li J, Bushel PR. EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data. BMC Genomics. 2016 Mar 22;17(1):255.

Bushel PR, Fannin RD, Gerrish K, Watkins PB, Paules RS. Blood gene expression profiling of an early acetaminophen response. Pharmacogenomics J. 2016 Mar 1.

*Wang C, *Gong B, *Bushel PR, et al. SEQC Consortium, The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol. 2014 Sep;32(9):926-32.

Huang L, Zhang HH, Zeng ZB, Bushel PR. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer Inform. 2013 Aug 4;12:143-53.

Lu J, Bushel PR. Dynamic expression of 3′ UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: implications in gene expression profiling. Gene. 2013 Sep 25;527(2):616-23.

Bushel PR, McGovern R, Liu L, Hofmann O, Huda A, Lu J, Hide W, Lin X. Population differences in transcript-regulator expression quantitative trait loci. PLoS One. 2012;7(3):e34286. Epub 2012 Mar 27.

Bushel PR, Heard NA, Gutman R, Liu L, Peddada SD, Pyne S. Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle BMC Systems Biology. 2009 Sept 16;3:93.

*Bushel, P.R., *Heinloth, A.N., Li, J., Huang, L., Chou, J.W., Boorman, G.A., Malarkey, D.E., Houle C.D., Ward S.M., Wilson R.E., Tennant R.W., Paules, R.S. Blood Gene Expression Signatures Predict Exposure Levels. PNAS 2007 Nov;104(46):18211-18216. Epub 2007 November 2.