What if subtle changes in gene expression could unlock answers to undiagnosed genetic diseases? This session will explore how allelic imbalance serves as a powerful signal for detecting gene regulatory defects. By using advanced statistical modeling and deep learning, the work improves the analysis of sequencing data and reporter assays to better identify inherited patterns of gene regulation. The findings also shed light on regulatory mechanisms and reveal how harmful mutations in noncoding DNA are selected against. 

This session will be led by Bill Majoros, an assistant professor in the Department of Biostatics at Duke University. He holds a bachelor’s degree in computer science from Penn State and a doctorate in computational biology from Duke University. Majoros has more than 30 years of experience as an applied researcher with work spanning defense electronic, natural language processing and genomics. In the early 2000s, he contributed to the initial sequencing and annotation of the human genome at Celera Genomics, which led to his 2007 monograph on algorithms for gene structure prediction published by Cambridge University Press. His laboratory at Duke focuses on developing computational methods to better understand genetic mechanisms in gene regulation.