Evidence map›Paper›PMID 37282589›Full record

ArticleJournal of the Royal Society, Interface2023

Single-cell Ca

Xiaojun Wu, Roy Wollman, Adam L MacLean

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiaojun WuDepartment of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.ORCID 0000-0002-7613-8415
Roy WollmanDepartment of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA.
Adam L MacLeanDepartment of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.ORCID 0000-0003-0689-7907

Funding

Computational methods to predict gene regulatory network dynamics and cell state transitionsR35GM143019 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MACLEAN, ADAM L · 2021 to 2025
$2.1M
NIGMS NIH HHS R35 GM143019
6 · The paper itself

Abstract

Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca

Indexed as

GenomicsSignal TransductionBayes TheoremBayesian inferencecalcium signallingdynamical systemssingle cellspatial transcriptomics

Identifiers

PMID37282589
PMCPMC10244972

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.