Evidence map›Paper›PMID 39812978›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Data Treatment for LC-MS Untargeted Analysis.

Mar Garcia-Aloy, Johannes Rainer, Pietro Franceschi

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. 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

3 authors.

Mar Garcia-AloyResearch and Innovation Centre, Fondazione E. Mach, Trento, Italy.
Johannes RainerInstitute for Biomedicine, Eurac Research, Bolzano, Italy.
Pietro FranceschiResearch and Innovation Centre, Fondazione E. Mach, Trento, Italy. pietro.franceschi@fmach.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted experiments require complex bioinformatic strategies to extract information from the experimental data. Here we discuss the "data preprocessing," the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.

Indexed as

Computational BiologyMass SpectrometryMetabolomicsChromatography, LiquidHumansLiquid Chromatography-Mass SpectrometrySoftwareMetadataMissing valuesPeak pickingPreprocessingQuality checkRetention time correction

Identifiers

PMID39812978

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.