Advancing Environmental Health Research with Artificial Intelligence and Machine Learning: Session I — AI & ML Applications to Understand Chemical Mixtures, Properties, and Exposures and Their Relationship to Human Health (Nov 4, 2024)

Contaminated Site Clean-Up Information (CLU-IN): Internet Seminar Video Archives

The NIEHS Superfund Research Program (SRP) is hosting a Risk e-Learning webinar series focused on using artificial intelligence (AI) and machine learning to advance environmental health research. The series will feature SRP-funded researchers, collaborators, and other subject-matter experts who aim to better understand and address environmental health issues by applying AI and machine learning approaches to complex issues. Recent advances in AI and machine learning methods show promise to improve the accuracy and efficiency of environmental health research. Over the course of three sessions, presenters will discuss how they use AI and machine learning approaches to improve chemical analysis, characterize chemical risk, understand microbial ecosystems, develop technologies for contaminant removal, and more. In the first session, AI & ML Applications to Understand Chemical Mixtures, Properties, and Exposures and their Relationship to Human Health, speakers will discuss how they apply machine learning and artificial intelligence techniques to understand chemical exposures and their effects on human health. To learn about and register for the other sessions in this webinar series, please see the SRP website. Naomi Halas, Ph.D., and Ankit Patel, Ph.D., will share updates on their work combining surface-enhanced spectroscopies (Raman and Infrared Absorption) with machine learning algorithms with the goal of developing simple and ultimately low-cost methods for the detection and identification of environmental toxins. As part of their discussion, they will share several approaches, including the use of machine learning algorithms to detect individual constituents in complex mixtures and the use of facial recognition strategies to identify specific chemical toxins in human placenta. Jacob Kvasnicka, Ph.D., will present on a project he supported while he was a postdoctoral researcher at Texas A&M University SRP Center's Risk and Geospatial Sciences Core. There, his work involved developing an ML framework for predicting safe exposure levels to chemicals to avoid cancerous and reproductive/developmental effects. Most chemicals lack toxicity data related to human health, and this study uses ML to fill this gap, greatly expanding the ability to characterize chemical risks and impacts. Trey Saddler will give attendees an overview of ToxPipe — a platform for performing retrieval augmented generation (RAG) over toxicological data. Comprised of a web interface, agentic workflows, and connections to various data sources, ToxPipe enables toxicologists to explore diverse datasets and generate toxicological narratives for a wide range of compounds. Speakers:Naomi Halas, Ph.D., and Ankit Patel, Ph.D., Rice UniversityJacob Kvasnicka, Ph.D., U.S. Environmental Protection AgencyTrey Saddler, NIEHS, Division of Translational ToxicologyModerator: David Reif, Ph.D., NIEHS, Division of Translational Toxicology To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/SRP-ML-AI1_110424/