ArticleNature communications2017
Sensitive detection of rare disease-associated cell subsets via representation learning.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed, 184 citations in OpenAlex.
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- Accurate automated diagnosis of B-acute lymphoblastic leukemia using deep learning and flow cytometry.Haematologica · 2026Article
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- VoxelCoder: Classification of human cellular phenotypes via autoencoder batch alignment and hyperdimensional representation of cytometry data.Patterns (New York, N.Y.) · 2026Article
- Navigating the Landscape of Cytometry-Based Single-Cell Proteomics: Quantification, Annotation, and Resources.International journal of molecular sciences · 2026Review
- Towards a Cytometry Foundation Model: Interpretable Sample-level Predictive Modelling via Pretrained Transformers.bioRxiv : the preprint server for biology · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- Application and characterization of the multiple instance learning framework in flow cytometry.Scientific reports · 2026Article
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- TissueFormer: a neural network for labeling tissue from grouped single-cell RNA profiles.bioRxiv : the preprint server for biology · 2025Article
- Microglia Single-Cell RNA-Seq Enables Robust and Applicable Markers of Biological Aging.Aging cell · 2025Article
- Single-Molecule Enzyme Activity Analysis for Illuminating Pathological Proteoforms.ACS central science · 2025Review
- Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.Nature communications · 2025Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%.
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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.