{
  "abstract": "Mass spectrometry proteomics and metabolomics generate high-dimensional quantitative matrices requiring rigorous statistical and machine learning evaluation, yet existing analytical workflows typically depend on programmatic scripting or remote cloud servers that introduce datagovernance constraints. ProtBed is a client-side web application that executes post-extraction data exploration directly in the browser runtime. The platform integrates deterministic preprocessing, pairwise and omnibus hypothesis testing, six-model cross-validated machine learning, and publication-ready visualization while preserving complete local custody of quantitative abundance matrices and clinical metadata. Empirical validation was conducted across four diverse cohorts totaling 250 clinical specimens. On an untargeted GC-MS urine cohort (𝑛 = 40; 356 metabolites), stratified five-fold cross-validated classifiers discriminated diabetic kidney disease with AUC = 0.90 (89% sensitivity, 82% specificity). In a deep-coverage post-mortem substantia nigra tissue cohort (𝑛 = 30; 9,606 quantified proteins), full-matrix modeling achieved out-of-fold AUC = 0.99 using logistic regression (AUC = 0.96 for linear support vector machines) and prioritized neuroinflammatory mediators including SAA1, CRP, and GFAP. ProtBed further resolved multiclass diagnostic arms in plasma extracellular-vesicle proteomics (𝑛 = 124) and characterized dysregulated lipid classes in Alzheimer's disease plasma (𝑛 = 56). ProtBed is freely accessible at https://protbed.com/ .",
  "abstract_shown": true,
  "affiliations": [
    "ProtBed"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anas Bedraoui",
      "orcid": "0000-0003-4069-5575"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0wb9w1wr14jmr.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010689/v1",
  "fulltext_url": null,
  "id": "0wb9w1wr14jmr",
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  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010689/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Metabolomics and Mass Spectrometry Studies"
    ]
  ],
  "openalex": "W7222993716",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010689/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0wb9w1wr14jmr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010689",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010689/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Metabolomics and Mass Spectrometry Studies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "ProtBed: a streamlined web platform for exploratory statistics and predictive modeling in quantitative mass spectrometry",
  "title_text": "ProtBed: a streamlined web platform for exploratory statistics and predictive modeling in quantitative mass spectrometry",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/0wb9w1wr14jmr",
  "venue": "ChemRxiv",
  "versions": []
}