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, pathology images 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 RNA-Seq 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.