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PreprintCC BYChemRxiv

ProtBed: a streamlined web platform for exploratory statistics and predictive modeling in quantitative mass spectrometry

  • Anas Bedraoui1
  1. 1ProtBed
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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/ .

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Cite

BibTeXRIS
BibTeX
@misc{bedraoui2026protbed,
  title = {{ProtBed: a streamlined web platform for exploratory statistics and predictive modeling in quantitative mass spectrometry}},
  author = {Anas Bedraoui},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010689/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010689/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - ProtBed: a streamlined web platform for exploratory statistics and predictive modeling in quantitative mass spectrometry
AU  - Anas Bedraoui
PY  - 2026
DA  - 2026/10/20
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010689/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010689/v1
AB  - 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/ .
ER  -