Advancing Environmental Health Research with Artificial Intelligence and Machine Learning: Session III — ML & AI Applications to Understand Omics, Metabolomics, & Immunotoxicity and Optimizing Bioengineering Using Datasets, Models, & Mass Spectromet

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 third and final session, ML & AI Applications to Understand Omics, Metabolomics, & Immunotoxicity and Optimize Bioengineering Using Datasets, Models, and Mass Spectrometry, speakers will discuss how they apply machine learning and artificial intelligence tools to analyze mass spectrometry and microscopy data and optimize models for understanding metabolomics, metabolite pathways, and immunotoxicology To learn about and register for the other sessions in this webinar series, please see the SRP website. Grace Peng, Ph.D., is a co-coordinator of the National Institutes of Health (NIH) Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program, bridging the gap between the biomedical, behavioral and bioethics research communities and the data science/AI communities through a consortium of diverse experts to set the stage for widespread adoption of AI/ML in medicine. Dr. Peng will give an overview of the Bridge2AI program and introduce one of their projects at the University of California San Diego — Trey Ideker, Ph.D. Dr. Ideker will discuss the cell maps for AI (CM4AI) functional genomics project, one of four major data generation projects under the Bridge2AI program. The goal of the project is to provide a comprehensive map of human cellular components through generation of major spatial proteomics datasets. John Efromson, M.S., will present on Ramona Optic, Inc.'s Multi-Camera Array Microscope [MCAM(TM)], which is used to automate imaging and computer vision analysis of zebrafish and greatly improves previous throughput and analysis capabilities. Multiple applications of machine learning will be discussed, including behavioral pose estimation and phenotyping, morphological analysis, and cell counting and fluorescence quantification, as well as how these distinct analyses can be used together for pharmacology, toxicology, and neuroscience research. Speakers:Grace C.Y. Peng, Ph.D., Division of Discovery Science and Technology (Bioengineering), National Institute of Biomedical Imaging and Bioengineering and Trey Ideker, Ph.D., University of California San DiegoJohn Efromson, M.S., Ramona OpticsForest White, Ph.D., Massachusetts Institute of Technology (MIT)Moderator: Hunter Moseley, Ph.D., University of Kentucky To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/SRP-ML-AI3_112224/