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MALDI-TOF Mass Spectrometry

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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Updated: Mar 19, 2026

Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

Published on: November 26, 2013

A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data.

Manuel Galli¹, Italo Zoppis², Gabriele De Sio¹

¹Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza Brianza, Italy.

Advances in Bioinformatics

|June 14, 2016

Summary

This summary is machine-generated.

Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) combined with machine learning accurately classifies thyroid lesions. This approach identifies key protein biomarkers, improving diagnostic efficiency and reliability for complex diseases.

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MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules

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Last Updated: Mar 19, 2026

Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

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MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules

MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules

Published on: March 5, 2014

Area of Science:

Biomarker discovery and
omics
proteomics

Background:

Multifactorial diseases require robust biomarkers for accurate characterization and prediction.
Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) generates complex data, necessitating advanced computational methods.
Accurate diagnosis of thyroid lesions, particularly indeterminate cases (THY3), is crucial due to their potential malignancy.

Purpose of the Study:

To develop an accurate classification model for thyroid bioptic specimens using MALDI-MSI data.
To leverage machine learning and feature selection for efficient biomarker discovery.
To identify specific tissue areas with potential diagnostic value.

Main Methods:

Application of Support Vector Machines (SVM), a state-of-the-art machine learning algorithm.
Utilisation of recursive feature elimination (RFE) for wrapper feature selection.
Analysis of MALDI-MSI data from thyroid bioptic specimens.

Main Results:

Achieved accurate classification of thyroid bioptic specimens.
Reduced feature set to 20 out of 144, enhancing model performance, reliability, and computational efficiency.
Classified specific tissue areas, pinpointing potential discriminating regions.

Conclusions:

Machine learning applied to MALDI-MSI data provides an effective tool for biomarker discovery in complex diseases.
The developed model offers improved diagnostic accuracy and efficiency for thyroid lesions.
Identifying specific tissue areas enhances the clinical relevance of proteomic profiling.