ArticleAnalytical chemistry2024
Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration.
Article in Analytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- MSIDAT: an automated platform for improved metabolite annotation in mass spectrometry imaging via mass shift evaluation and customized databases.Analytical and bioanalytical chemistry · 2026Article
- Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.Analytical and bioanalytical chemistry · 2026Review
- A Novel Peak-Shape Aware Approach for Mass Alignment in Mass Spectrometry.Rapid communications in mass spectrometry : RCM · 2026Article
- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
- The progress of biomarkers detection to lung cancer.Discover oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful imaging method for generating molecular maps of biological samples and has numerous applications in biomedical research. A key challenge in MALDI MSI is to reliably map observed mass peaks to theoretical masses, which can be difficult due to mass shifts that occur during the measurement process. In this paper, we propose MassShiftNet, a novel self-supervised learning framework for mass recalibration. We train a neural network on a data dependent and specifically augmented training data set to directly estimate and correct the mass shift in the observed spectra. In our evaluation, we show that this method is both able to reduce the absolute mass error and to increase the relative mass alignment between peptide MSI spectra acquired from FFPE-fixated tissue using a MALDI time-of-flight (TOF) instrument.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.