ArticleJournal of proteome research2026
The Omics Molecule Extractor: A Web Application for the Selection of Potential Biomarker Panels.
Article in Journal of proteome research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- The Omics Molecule Extractor: A Web Application for the Selection of Potential Biomarker Panels.Journal of proteome research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selecting molecular panels that are applicable to classify the health state of patients is a common task in omics data analysis. Existing software for molecule selection lacks features to select molecule panels from large data sets, requires programming experience, or lacks user-friendly interfaces. We present the Omics Molecule Extractor (OMEx), an open-source web application providing a user-friendly workflow for selecting molecules and molecule panels for sample classification from large data sets. OMEx's user interface provides interactive visualization for exploring input data and analysis results. The feature selection strategy underlying the algorithm is based on machine learning and has not been available in any software with a user interface. Extensive testing using synthetic data sets with known ground truth showed that the algorithm discovers group-separating molecules with high precision. Additionally, OMEx was tested on five real-world omics data sets, demonstrating high reproducibility and overlap with reported molecules from other feature selection methods, while also reporting alternative molecules of interest. OMEx is freely available at https://mdoa-tools.bi.denbi.de/omex/home.
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Registered trials
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