Evidence map›Paper›PMID 42681292›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Data Processing and Machine Learning Methods to Analyze MALDI-TOF MS Data.

Lucas Cardoso Lazari, Livia Rosa-Fernandes, Suely K N Marie, Antonio Di Ieva, Giuseppe Palmisano

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lucas Cardoso LazariGlycoProteomics Laboratory, Department of Parasitology, ICB, University of São Paulo, São Paulo, Brazil.
Livia Rosa-FernandesGlycoProteomics Laboratory, Department of Parasitology, ICB, University of São Paulo, São Paulo, Brazil.
Suely K N MarieLaboratory of Molecular and Cellular Biology, Lim 15, Department of Neurology, Faculdade de Medicina USP, São Paulo, Brazil.
Antonio Di IevaComputational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Giuseppe PalmisanoGlycoProteomics Laboratory, Department of Parasitology, ICB, University of São Paulo, São Paulo, Brazil. palmisano.gp@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MALDI-TOF MS is a rapid, sensitive method for generating complex protein profiles from minimal sample volumes. Although its spectra lack direct peptide sequence information, combining MALDI-TOF output with machine learning enables discrimination of diseases based on intensity patterns. Accurate machine learning model development depends on standardized preprocessing to reduce noise, dimensionality, and bias. This protocol demonstrates two key preprocessing approaches-peak picking and spectral binning-and demonstrates their application in training and testing a machine learning model for the diagnosis of glioblastoma.

Indexed as

Machine LearningSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationGlioblastomaHumansDiagnosisMachine learningMALDI-TOF MSPreprocessingPrognosis

Identifiers

PMID42681292

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.