Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles

Lucas C. Lazari; Livia Rosa-Fernandes; Giuseppe Palmisano

doi:10.1007/978-1-0716-2395-4_29

Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles

Lucas C. Lazari, Livia Rosa-Fernandes, Giuseppe Palmisano

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

Abstract

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

Original language	English
Title of host publication	Multiplex biomarker techniques
Subtitle of host publication	methods and applications for COVID-19 disease diagnosis and risk stratification
Editors	Paul C. Guest
Place of Publication	New York, NY
Publisher	Springer, Springer Nature
Chapter	29
Pages	375-394
Number of pages	20
ISBN (Electronic)	9781071623954
ISBN (Print)	9781071623947
DOIs	https://doi.org/10.1007/978-1-0716-2395-4_29
Publication status	Published - 2022
Externally published	Yes

Publication series

Name	Methods in Molecular Biology
Publisher	Humana Press
Volume	2511
ISSN (Print)	1064-3745

Keywords

Biomarkers/analysis
COVID-19/diagnosis
Humans
Machine Learning
Proteins
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods

Access to Document

10.1007/978-1-0716-2395-4_29

Cite this

Lazari, L. C., Rosa-Fernandes, L., & Palmisano, G. (2022). Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles. In P. C. Guest (Ed.), Multiplex biomarker techniques: methods and applications for COVID-19 disease diagnosis and risk stratification (pp. 375-394). (Methods in Molecular Biology; Vol. 2511). Springer, Springer Nature. https://doi.org/10.1007/978-1-0716-2395-4_29

Lazari, Lucas C. ; Rosa-Fernandes, Livia ; Palmisano, Giuseppe. / Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles. Multiplex biomarker techniques: methods and applications for COVID-19 disease diagnosis and risk stratification. editor / Paul C. Guest. New York, NY : Springer, Springer Nature, 2022. pp. 375-394 (Methods in Molecular Biology).

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title = "Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles",

abstract = "Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.",

keywords = "Biomarkers/analysis, COVID-19/diagnosis, Humans, Machine Learning, Proteins, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods",

author = "Lazari, {Lucas C.} and Livia Rosa-Fernandes and Giuseppe Palmisano",

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language = "English",

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Lazari, LC, Rosa-Fernandes, L & Palmisano, G 2022, Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles. in PC Guest (ed.), Multiplex biomarker techniques: methods and applications for COVID-19 disease diagnosis and risk stratification. Methods in Molecular Biology, vol. 2511, Springer, Springer Nature, New York, NY, pp. 375-394. https://doi.org/10.1007/978-1-0716-2395-4_29

Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles. / Lazari, Lucas C.; Rosa-Fernandes, Livia; Palmisano, Giuseppe.
Multiplex biomarker techniques: methods and applications for COVID-19 disease diagnosis and risk stratification. ed. / Paul C. Guest. New York, NY: Springer, Springer Nature, 2022. p. 375-394 (Methods in Molecular Biology; Vol. 2511).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles

AU - Lazari, Lucas C.

AU - Rosa-Fernandes, Livia

AU - Palmisano, Giuseppe

PY - 2022

Y1 - 2022

N2 - Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

AB - Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

KW - Biomarkers/analysis

KW - COVID-19/diagnosis

KW - Humans

KW - Machine Learning

KW - Proteins

KW - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods

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DO - 10.1007/978-1-0716-2395-4_29

M3 - Chapter

C2 - 35838976

SN - 9781071623947

T3 - Methods in Molecular Biology

SP - 375

EP - 394

BT - Multiplex biomarker techniques

A2 - Guest, Paul C.

PB - Springer, Springer Nature

CY - New York, NY

ER -

Lazari LC, Rosa-Fernandes L, Palmisano G. Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles. In Guest PC, editor, Multiplex biomarker techniques: methods and applications for COVID-19 disease diagnosis and risk stratification. New York, NY: Springer, Springer Nature. 2022. p. 375-394. (Methods in Molecular Biology). doi: 10.1007/978-1-0716-2395-4_29

Machine learning approaches to analyze MALDI-TOF mass spectrometry protein profiles

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