Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma-Reference-Cited by-同舟云学术

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Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Published:2024 Issue: Volume: Page:179-195
ISSN:1064-3745
Container-title:Methods in Molecular Biology
language:en
Short-container-title:

Author:

Grønning Alexander G. B.,Schéele Camilla

Publisher

Springer US

Link

https://link.springer.com/content/pdf/10.1007/978-1-0716-3646-6_9

Reference40 articles.

1. Muttenthaler M, King GF, Adams DJ, Alewood PF (2021) Trends in peptide drug discovery. Nat Rev Drug Discov 20:309–325. https://doi.org/10.1038/s41573-020-00135-8

2. Dallas DC, Guerrero A, Parker EA et al (2015) Current peptidomics: applications, purification, identification, quantification, and functional analysis. Proteomics 15:1026–1038. https://doi.org/10.1002/pmic.201400310

3. Lau JL, Dunn MK (2018) Therapeutic peptides: historical perspectives, current development trends, and future directions. Bioorg Med Chem 26:2700–2707. https://doi.org/10.1016/j.bmc.2017.06.052

4. Müller TD, Finan B, Bloom SR et al (2019) Glucagon-like peptide 1 (GLP-1). Mol Metab 30:72–130. https://doi.org/10.1016/j.molmet.2019.09.010

5. Fricker LD (2015) Limitations of mass spectrometry-based Peptidomic approaches. J Am Soc Mass Spectrom 26:1981–1991. https://doi.org/10.1007/s13361-015-1231-x

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