(183b) Development of the Texas a&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis

The National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program (SRP) aims to support university-based multidisciplinary research on human health and environmental issues related to hazardous substances and pollutants [1]. The Texas A&M Superfund Research Program comprehensively evaluate the complexities of hazardous chemical exposures, potential adverse health impacts, and potential hazards of exposures to complex mixtures through a number of multi-disciplinary projects and cores [2]. One of the essential components of the Texas A&M Superfund Research Center is the Data Science Core, which serves as the basis for translating the data produced by the multi-disciplinary research projects into useful knowledge for the community via data collection, quality control, analysis, and model generation. The four main projects of the TAMU Superfund Research Center mainly focuses on (i) understanding dynamic exposure pathways under the conditions of environmental emergencies, (ii) designing novel broad-acting sorption materials for reducing bioavailability of contaminants, (iii) studying in vitro and in vivo hazard, kinetics and inter-individual variability of responses to chemical mixtures, and (iv) developing in vitro multiplex single-cell assays to detect endocrine disruption potential of mixtures. Using the data generated by the projects, the overall goal is to develop comprehensive tools and models to address the environmental and health effects of exposure to chemical mixtures during environmental emergency-related contamination events. In this presentation, we demonstrate the Texas A&M Superfund Research Program computational platform, which houses and integrates large-scale, diverse data sets generated across the Center, and provides basic visualization service to facilitate interpretation, monitors the data quality, and finally implements variety of state-of-the-art data analytics tools for knowledge extraction [3]. Specifically, we use both supervised (i.e. classification, regression analysis) and unsupervised learning algorithms (i.e hierarchical, k-means, spectral clustering analysis) along with dimensionality reduction techniques to create decision support models and tools. The platform is aimed to facilitate effective integration and collaboration across the Center.

References

[1] National Institute of Environmental Health Sciences. Superfund Research Program (2018). https://www.niehs.nih.gov/research/supported/centers/srp/index.cfm (accessed 9 April 2018

[2] TAMU Superfund Research Center (2017). https://superfund.tamu.edu/ (accessed 9 April 2018).

[3] Onel, M.; Beykal, B.; Wang, M.; Grimm, F.A.; Zhou, L.; Wright, F.A.; Phillips, T.D.; Rusyn, I.; Pistikopoulos, E.N. Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques. Proceedings of the 28th European Symposium on Computer Aided Process Engineering, 2018, Graz, Austria (Accepted).