Computational Toxicology and Data Sciences
Serves as a gateway to data intensive, quantitative methods in toxicology and genomic sciences including complex genomic, transcriptomic, proteomic, metabolic and human population data.
Dr. Carolyn Mattingly
The Mattingly Lab aims to understand the effects of environmental chemicals on development and human health through the combinatorial use of bioinformatics and experimental models. Since 2001, she has been directing development of the publicly available CTD, which provides integrated data about cross-species chemical- gene/protein interactions, chemical- and gene-disease relationships, and exposure science data. She also directs a laboratory that leverages zebrafish as a model to understand pathways that are perturbed by chemical exposures during development and is actively pursuing translational projects that span zebrafish and human systems with a focus on behavioral and metabolic outcomes.
Dr. David Reif
Reif’s research group focuses on analytical methods development, visualization, and software to integrate high-dimensional genetic and environmental health data. The overarching research goal is to understand the complex interactions between human health and the environment through the analysis of high-dimensional data from diverse sources. His lab designs experiments, develops methods, and implements software to distill useful information via the integration of complex data.
Dr. Fred Wright
Dr. Wright’s lab’s interests include gene mapping, methodology development in the statistical analysis of expression microarrays, somatic genomics of cancer, genome-wide association studies, GxE interactions and expression quantitative trait locus (eQTL) analysis
Zhou’s group’s research interests include statistical genetics of GxE interactions, machine learning methods, high dimensional low sample size (HDLSS) data analysis, and next generation and third generation sequencing models.