ArticleNature communications2024
Restricting datasets to classifiable samples augments discovery of immune disease biomarkers.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Expanding the supply of human leukocytes for research by harvesting cell concentrates from Trima Accel tubing sets.Scientific reports · 2026Article
- Untargeted Plasma Proteomic Signatures and Late Graft Failure in Kidney Transplant Recipients.Transplantation · 2026Article
- Extracorporeal photopheresis for pembrolizumab-induced dermatitis: a case report.Skin health and disease · 2025Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Platelet apheresis with additive solution and plasma rinseback affects the cellular composition of LRS chamber products.Scientific reports · 2025Article
- Restricting datasets to classifiable samples augments discovery of immune disease biomarkers.Nature communications · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation - namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker's informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.
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