Lipidomics Predictive Modeling using the R Statistical Language Caret Package

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Course Description

Predictive modeling for high-throughput lipid analysis is an important tool to interpret outcomes associated with health and disease. Although commercially available lipid analysis softwares have some of these capabilities, they lack model development and validation flexibility. This seminar introduces the basics of predictive modeling with real-world lipidomics data using R, a free statistical computing language with hundreds of modeling algorithms and a unified coding architecture. 

Meet the Instructor

Brian Piccolo is an Assistant Professor at the Arkansas Children’s Nutrition Center and has > 10 years of experience specializing in metabolomics data analysis.  He was trained in multivariate and machine learning based approaches to untargeted metabolomics and lipidomics as a Postdoctoral Fellow at the West Coast Metabolomics Center and the USDA-Western Human Nutrition Research Center in Davis CA. At the ACNC, Dr. Piccolo is the Associate Director of the ACNC Biostatistics and Data Innovation group and supports –omics based research to the center. He also served as a Statistical Review Board Member for the American Society of Nutrition (2019-2022) and has provided local and national courses focused on data analysis approaches using the R Statistical Language.