Presentation by Ryan Huebinger Associate Professor of Emergency Medicine University of New Mexico Health Sciences Center Real-world datasets are power tools for conducting low-cost or free research and generating preliminary data for grant applications. In this lecture, Dr. Huebinger covers different types of real-world data in addition to their strengths and weakness. He will then use an example from his own research to show how these data can be used to address research questions in novel ways. Register to join via zoom

Application: Fill out the online application here: Differential Expression Workshop Application Application due by October 13, 2025 When October 20-24, 2025 (5 days) 9 am – 4 pm Where Virtual (by the National Center for Genome Resources (NCGR) Santa Fe, NM) Topic Overview Analysis of gene expression changes in response to different treatments or conditions can yield important biological insights, including elucidating abiotic and biotic stress responses, uncovering disease and resistance mechanisms, and highlighting developmental changes. In this workshop you will learn how to perform differential gene expression, visualization, and pathway analysis. In addition, you will learn how to perform coexpression analysis and identify genes driving expression shifts.

A presentation by: Mindy Hébert-DeRouen, PhD, MPH Assistant Professor Department of Public Health Sciences College of Health, Education, and Social Transformation Use of EHR-based cohorts for research requires informed extraction, harmonization, and operationalization of EHR data. Dr. Hebert-DeRouen will describe the development of an EHR-based cohort with data from three healthcare systems linked to population-based cancer registry data to examine clinical and neighborhood factors that contribute to racial and ethnic disparities in liver cancer. She will detail operationalization of detailed race/ethnicity (17 categories) and liver cancer risk factors from EHR data; illustrate the utility of linking EHR data to cancer registry data; and present results on racial/ethnic disparities in liver cancer as well as the relative prevalence and contribution of risk factors to liver cancer diagnosis across racial/ethnic groups.