Systems Science Friday Noon Seminar Series
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Format
Video: MP4; File size: 419 MB; Duration: 58:19
Date
2-17-2023
Abstract
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.
Biographical Information
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.
Subjects
Reconstructability Analysis, Information Theory, Lattice Theory
Disciplines
Systems Science
Persistent Identifier
https://archives.pdx.edu/ds/psu/39335
Rights
© 2023 Tristan Holmes
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Recommended Citation
Holmes, Tristan, "The LUST Algorithm: A Discrete Mathematical Method for Analyzing Genetic Expression Data" (2023). Systems Science Friday Noon Seminar Series. 127.
https://archives.pdx.edu/ds/psu/39335