Video: MP4; File size: 419 MB; Duration: 58:19
The Lattice Upstream Targeting Algorithm applies the theory of finite ordered sets to create a predictive model of outcomes for cancer patients based on genetic expression data from biopsied tumor samples. In this talk we offer a brief introduction to the mathematical background behind the algorithm. We then examine how the algorithm identifies genetic signatures significant to patient outcomes and uses clinical data to create a predictive model of patient survival. Using publicly available data of cancer patients, we examine a model that is created by applying LUST, and discuss possible future applications.
Tristan Holmes received his PhD in Mathematics from the University of Hawaii at Manoa for his dissertation "Inflation of Finite Lattices Along All-or-Nothing Sets." His research interests include lattice theory, universal algebra, and machine learning. He has served as full time teaching faculty at the UH Manoa, as well as part time teaching faculty at Windward Community College, Portland State University, and Portland Community College.
Reconstructability Analysis, Information Theory, Lattice Theory
© 2023 Tristan Holmes
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Holmes, Tristan, "The LUST Algorithm: A Discrete Mathematical Method for Analyzing Genetic Expression Data" (2023). Systems Science Friday Noon Seminar Series. 127.