Degree Name

Department

Mathematics

College

Physical and Mathematical Sciences

Defense Date

2019-03-01

Publication Date

2019-03-15

First Faculty Advisor

John C. Price

First Faculty Reader

Samuel Payne

Honors Coordinator

Paul Jenkins

Keywords

mass spectrometry, machine learning, programming, biochemistry, brain, lipidomics

Abstract

Mass spectrometry provides an extensive data set that can prove unwieldy for practical analytical purposes. Applying programming and machine learning methods to automate region analysis in DESI mass spectrometry of mouse brain tissue can help direct and refine such an otherwise unusable data set. The results carry promise of faster, more reliable analysis of this type, and yield interesting insights into molecular characteristics of regions of interest within these brain samples. These results have significant implications in continued investigation of molecular processes in the brain, along with other aspects of mass spectrometry, collective analysis of biological molecules (i.e. omics), and biology in general.