Physical and Mathematical Sciences
First Faculty Advisor
John C. Price
First Faculty Reader
mass spectrometry, machine learning, programming, biochemistry, brain, lipidomics
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.
BYU ScholarsArchive Citation
Ahlstrom, Austin, "Computational Regiospecific Analysis of Brain Lipidomic Profiles" (2019). Undergraduate Honors Theses. 70.