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Texas A&M University

Superfund Research Program

Data Science Core

Project Leader: Stratos Pistikopoulos
Co-Investigator: Fred A. Wright (North Carolina State University)
Grant Number: P42ES027704
Funding Period: 2017-2022
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2017-2022)

The objective of the Texas A&M University Superfund Research Program (TAMU SRP) Center is to explore and develop descriptive models and tools that can predict the possible hazardous outcomes of chemical exposure during environmental emergencies and to produce powerful solutions which can mitigate the negative effects on human health. The ultimate goal of the Center is to contribute to decision-making capabilities for planning and control in emergency environmental contamination events.

The Data Science contributes to achieving the goals of the Center by supporting the work of the four research projects. The projects produce high-dimensional data that requires comprehensive analysis and expertise in state-of-the-art data science methodologies in order to translate raw experimental data into actionable insights and predictive models.

The Data Science Core provides numerous methods and services to the Center researchers by:

  • Sharing expertise and providing support via advanced methodologies in data science and statistics
  • Developing high-performance, novel methods for simultaneous regression or classification with dimensionality reduction and data integration
  • Constructing and maintaining a computational platform that will enable collaboration across the Center and facilitate dissemination of knowledge to the wider community and key stakeholders

The Dynamic Exposure Pathways Under Conditions of Environmental Emergencies project characterizes exposure pathways of contaminated sediments that are vulnerable to movement and re-deposition due to storm activity; the Data Science Core provides services for experimental design, hypothesis testing, and regression for contaminated sediment binding experiments. The Mitigation of Chemical and Mixture Effects Through Broad-Acting Sorbents studies the mitigation of adverse health effects of chemicals through broad-acting sorption materials; the Data Science Core utilizes predictive modeling of sorption activity via advanced regression and simultaneous dimensionality reduction with nonlinear kernels to guide experimental design and material property identification. The Inter-Tissue and -Individual Variability in Response to Mixtures project investigates the inter-tissue and inter-individual variability in response to complex environmental mixtures; the Data Science Core applies composite classification and clustering strategies for characterization of chemical mixtures. The Single Cell, Multi-Parametric High Throughput Platform to Classify Endocrine Disruptor Potential of Mixtures project develops single-cell, high-throughput platforms to quantify the endocrine disruptor potential of environmental contaminants and mixtures; the Data Science Core aids in predicting the activity of multiple endocrine receptors through model construction and reduction of predictive models.

Furthermore, the Data Science Core maximizes productivity within the Center by establishing an ideal environment for data sharing and collaboration via a computational platform service. The Core also disseminates the results of the TAMU SRP Center, including access to the final high-performance predictive models and tools, by providing interactive interfaces amenable for use by the scientific community.

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