ArticleSTAR protocols2021
Quantitative neuronal morphometry by supervised and unsupervised learning.
Article in STAR protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Accelerating the continuous community sharing of digital neuromorphology data.FASEB bioAdvances · 2024Article
- Local Microtubule and F-Actin Distributions Fully Constrain the Spatial Geometry ofInternational journal of molecular sciences · 2023Article
- Cell morphologies in the nervous system: Glia steal the limelight.The Journal of comparative neurology · 2023Article
- Circuit analysis of theNetwork neuroscience (Cambridge, Mass.) · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
- Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.Org.Brain informatics · 2022Article
- A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes.Nature neuroscience · 2022Article
- Large scale similarity search across digital reconstructions of neural morphology.Neuroscience research · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. Then, we provide detailed steps on classification, clustering, and statistical analysis of the traced cells based on morphological features. We illustrate the pipeline design using specific examples from zebrafish anatomy. Our approach can be readily applied and generalized to the characterization of axonal, dendritic, or glial geometry. For complete context and scientific motivation for the studies and datasets used here, refer to Valera et al. (2021).
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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.