ArticleeLife2020
Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- DNA damage response profile distinguishes poor-acting gliomas with shared methylome signatures.Neuro-oncology · 2026Article
- AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome.Nature medicine · 2025Article
- Article
- Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.Journal of proteome research · 2025Review
- BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome.bioRxiv : the preprint server for biology · 2025Article
- A single-cell mass cytometry-based atlas of the developing mouse brain.Nature neuroscience · 2025Article
- IL-8 Instructs Macrophage Identity in Lateral Ventricle Contacting Glioblastoma.bioRxiv : the preprint server for biology · 2024Article
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Understanding virus-host interactions in tissues.Nature microbiology · 2023Review
- An immunosuppressed microenvironment distinguishes lateral ventricle-contacting glioblastomas.JCI insight · 2023Article
- Single-cell protein profiling defines cell populations associated with triple-negative breast cancer aggressiveness.Molecular oncology · 2023Article
- Learning cell identity in immunology, neuroscience, and cancer.Seminars in immunopathology · 2023Review
- Article
- Two New Neutrophil Subsets Define a Discriminating Sepsis Signature.American journal of respiratory and critical care medicine · 2022Observational
- Histological Studies of the Ventricular-Subventricular Zone as Neural Stem Cell and Glioma Stem Cell Niche.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2021Review
- MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY.Current opinion in systems biology · 2021Article
- An integrated pipeline for comprehensive analysis of immune cells in human brain tumor clinical samples.Nature protocols · 2021Review
- Article
- Analyzing high-dimensional cytometry data using FlowSOM.Nature protocols · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.
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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.