ReviewTrends in cell biology2018
Towards a Quantitative Understanding of Cell Identity.
Review in Trends in cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 41 citations in OpenAlex.
- Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila.Nucleic acids research · 2026Article
- Cytokeratin profile supports developmental origin of cerebellopontine angle epidermoid cysts.Brain & spine · 2026Article
- Application of Machine Learning to Discriminate Photoreceptor Cell Species in Xenotransplanted Chimeric Retinas.Clinical and translational science · 2025Article
- Transiently increased coordination in gene regulation during cell phenotypic transitions.PRX life · 2024Article
- Inertial effect of cell state velocity on the quiescence-proliferation fate decision.NPJ systems biology and applications · 2024Article
- Decoding Human Biology and Disease Using Single-cell Omics Technologies.Genomics, proteomics & bioinformatics · 2023Review
- Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs.Scientific reports · 2023Article
- Complex computation from developmental priors.Nature communications · 2023Article
- Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.International journal of molecular sciences · 2023Article
- Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells.Analytical chemistry · 2023Article
- Learning cell identity in immunology, neuroscience, and cancer.Seminars in immunopathology · 2023Review
- Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Article
- Lipidome profiling with Raman microspectroscopy identifies macrophage response to surface topographies of implant materials.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Iterative single-cell multi-omic integration using online learning.Nature biotechnology · 2021Article
- Molecular mechanisms of transcription factor mediated cell reprogramming: conversion of liver to pancreas.Biochemical Society transactions · 2021Review
- Trajectory-based energy landscapes of gene regulatory networks.Biophysical journal · 2021Article
- Advanced Materials to Enhance Central Nervous System Tissue Modeling and Cell Therapy.Advanced functional materials · 2020Article
- A multiresolution framework to characterize single-cell state landscapes.Nature communications · 2020Article
- Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging.Molecules and cells · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.
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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.