ReviewCytometry. Part A : the journal of the International Society for Analytical Cytology2020
A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.
Review in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed.
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- A novel MIR imaging approach for precise detection ofBiofilm · 2025Review
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- State-of-the-art cytometry in the search of novel biomarkers in digestive cancers.Frontiers in oncology · 2024Review
- Flow Cytometry: The Next Revolution.Cells · 2023Review
- Technology meets TILs: Deciphering T cell function in the -omics era.Cancer cell · 2023Review
- tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.Bioinformatics advances · 2023Article
- CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.Nature communications · 2022Article
- FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology.Blood advances · 2022Article
- Mass Cytometry for the Characterization of Individual Cell Types in Ovarian Solid Tumors.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Making the most of high-dimensional cytometry data.Immunology and cell biology · 2021Review
- K-means quantization for a web-based open-source flow cytometry analysis platform.Scientific reports · 2021Article
- Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data.International journal of molecular sciences · 2021Article
- High Dimensional Imaging Mass Cytometry Panel to Visualize the Tumor Immune Microenvironment Contexture.Frontiers in immunology · 2021Article
- Single-Cell Immune Profiling in Coronary Artery Disease: The Role of State-of-the-Art Immunophenotyping With Mass Cytometry in the Diagnosis of Atherosclerosis.Journal of the American Heart Association · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.
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What OpenQuestion holds
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.