ReviewFrontiers in immunology2021
Application of Machine Learning for Cytometry Data.
Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated and reproducible cell identification in mass cytometry using neural networks.Briefings in bioinformatics · 2023Pooled it
- ConvexGating infers gating strategies from clusters in single cell cytometry data.Nature communications · 2026Article
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- Decoding Leukocyte Dynamics: Functional Biomarkers and Precision Diagnostics in Parasitic Infections.Acta parasitologica · 2026Review
- Accurate automated diagnosis of B-acute lymphoblastic leukemia using deep learning and flow cytometry.Haematologica · 2026Article
- Towards a Cytometry Foundation Model: Interpretable Sample-level Predictive Modelling via Pretrained Transformers.bioRxiv : the preprint server for biology · 2026Article
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.Cytometry. Part B, Clinical cytometry · 2025Article
- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
- Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.Journal of proteomics · 2025Article
- Automation of flow cytometry data analysis with elastic image registration.Scientific reports · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Pan-cancer convergence of tumour-immune microenvironment motifs revealed by CyTOF and imaging mass cytometry.Frontiers in immunology · 2025Review
- Decoding sex differences in human immunity through systems immunology.Oxford open immunology · 2025Review
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Application of mass cytometry to characterize hematopoietic stem cells in apheresis products of patients with hematological malignancies.Hematology, transfusion and cell therapy · 2024Article
- AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.Scientific reports · 2024Article
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.Scientific reports · 2024Article
- Characterization of CD34Cancers · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.
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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.