# Data Science Core

> **NIH NIH P42** · TEXAS A&M UNIVERSITY · 2020 · $203,421

## Abstract

Data Science Core ABSTRACT
The objective of the Texas A&M Superfund Research 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 Core is one of the essential components of
the Center that will contribute to achieving the goals of the Center by supporting the work of four challenging
Research Projects. The projects will 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. Directed by Dr. Christodoulos A. Floudas and in collaboration with
Co-investigator Dr. Fred A. Wright, the Data Science Core will provide numerous methods and services to the
Center researchers under three specific aims: (i) by sharing expertise and providing support via advanced
methodologies in data science and statistics; (ii) by developing high-performance, novel methods for
simultaneous regression or classification with dimensionality reduction and data integration; and (iii) by
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. Research Project 1 will
characterize exposure pathways of contaminated sediments that are vulnerable to movement and re-
deposition due to storm activity; the Data Science Core will provide services for experimental design,
hypothesis testing, and regression for contaminated sediment binding experiments. Project 2 will study the
mitigation of adverse health effects of chemicals through broad-acting sorption materials; the Data Science
Core will utilize predictive modeling of sorption activity via advanced regression and simultaneous
dimensionality reduction with nonlinear kernels to guide experimental design and material property
identification. Project 3 will investigate the inter-tissue and inter-individual variability in response to complex
environmental mixtures; the Data Science Core will apply composite classification and clustering strategies for
characterization of chemical mixtures. Project 4 will develop single-cell, high-throughput platforms to quantify
the endocrine disruptor potential of environmental contaminants and mixtures; the Data Science Core will aid
in predicting the activity of multiple endocrine receptors through model construction and reduction of predictive
models. Furthermore, the Data Science Core will maximize productivity within the Center by establishing an
ideal environment for data sharing and collaboration via a computational platform service. The ...

## Key facts

- **NIH application ID:** 9903363
- **Project number:** 5P42ES027704-04
- **Recipient organization:** TEXAS A&M UNIVERSITY
- **Principal Investigator:** Christodoulos Achilleus Floudas
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $203,421
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9903363

## Citation

> US National Institutes of Health, RePORTER application 9903363, Data Science Core (5P42ES027704-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9903363. Licensed CC0.

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