ArticleNature biotechnology2026
Accurate quantification in proteomics with QuantUMS.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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Who cites it
5 citing papers in PubMed.
- Functional proteomic and phosphoproteomic profiling of routine FFPE CNS tumor tissue complements genomic and epigenomic characterization.Acta neuropathologica · 2026Article
- Leveraging high-throughput proteomics and AI-based protein folding to accelerate VAV1 molecular glue discovery.Nature communications · 2026Article
- Carnosine Potentiates a Compensatory Mitochondrial-Synaptic Proteomic Response in the ALS Cerebellum.Antioxidants (Basel, Switzerland) · 2026Article
- Systematic evaluation of PASEF acquisition strategies in complex metaproteomes.Nature communications · 2026Article
- Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.bioRxiv : the preprint server for biology · 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
6 authors.
Funding
Abstract
In mass-spectrometry-based proteomics it remains challenging to ensure the accuracy of protein quantities. Here we introduce QuantUMS (quantification using an uncertainty-minimizing solution), a machine learning-based method that dynamically tunes the quantification algorithm to minimize quantitative errors. When applied to data-independent acquisition proteomics, QuantUMS increases accuracy and precision, ameliorates ratio compression bias and enhances differential expression analysis. It further reports an uncertainty measure enabling quality control of individual quantities.
Identifiers
42204360What OpenQuestion 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 OpenQuestion graph.