Identifying antimicrobial peptides in genomes using machine learning

Fingerhut, Legana C. H. W. (2022) Identifying antimicrobial peptides in genomes using machine learning. PhD thesis, James Cook University.

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Legana Fingerhut used machine learning to improve predictions of antimicrobial peptides (AMPs) from protein sequences. Her associated framework was the first to specifically address the problem of identifying AMPs from whole-genome data. Her work leads to improved workflows for identifying novel AMPs which advances our understanding of the innate immune system.

Item ID: 78113
Item Type: Thesis (PhD)
Keywords: antimicrobial peptides, genome scanning, machine learning, R package, antimicrobial peptide prediction in R, ampir
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Copyright Information: Copyright © 2022 Legana C. H. W. Fingerhut.
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One publication arising from this thesis is stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publication is:

[Chapter 2] Fingerhut, Legana C. H. W., Miller, David J., Strugnell, Jan M., Daly, Norelle L., and Cooke, Ira R. (2020) ampir: an R package for fast genome-wide prediction of antimicrobial peptides. Bioinformatics, 36 (21). btaa653.

Date Deposited: 06 Apr 2023 02:10
FoR Codes: 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310109 Proteomics and intermolecular interactions (excl. medical proteomics) @ 100%
SEO Codes: 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified @ 100%
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