ReviewBiomedical chromatography : BMC2026
Mass Spectrometry-Based Chromatographic and Computational Workflows for Biomarker and Therapeutic Target Discovery: A Comprehensive Review.
Review in Biomedical chromatography : BMC, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of biomarkers and therapeutic targets is essential to the success of precision medicine. However, to reliably identify them, one must employ a combination of analytical and computational methods. This article describes the importance of high-quality study design, strict adherence to preanalytical and quality assurance practices and high levels of quality assurance as well as the performance characteristics of multiple types of chromatography/mass spectrometry to achieve the best possible results for sensitive, selective biomarker profiling in various biological fluids such as plasma, serum, urine, cerebrospinal fluid (CSF), and tissues. In addition, it explores the key workflows used in preparing biological samples (i.e., solid-phase extraction/specialised physical or chemical extractions/derivatisation), software/data processing pipelines for peak analysis (i.e., XCMS/MZmine/OpenMS/MS-DIAL) and processes for the identification of compounds by combining spectral libraries (e.g., Human Metabolome Database [HMDB], National Institute of Standards and Technology [NIST] and FiehnLib) with in silico tools (e.g., SIRIUS, CSI: FingerID, CANOPUS). Finally, it discusses several ways in which artificial intelligence/machine learning can be applied to the field of mass spectrometry for peak detection and provides a comprehensive review of the requirements for regulatory-grade validation and the workflow for targeted/multiplexed/quantitative liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC-MS) using stable isotope-labelled internal standards.
Indexed as
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What Socratic holds
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 Socratic graph.