Evidence map›Paper›PMID 38045474›Full record

ArticleArXiv2024

Reproducible image-based profiling with Pycytominer.

Erik Serrano, Srinivas Niranj Chandrasekaran, Dave Bunten, Kenneth I Brewer, Jenna Tomkinson, Roshan Kern, Michael Bornholdt, Stephen Fleming, Ruifan Pei, John Arevalo and 18 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

28 authors.

Erik SerranoDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Srinivas Niranj ChandrasekaranImaging Platform, Broad Institute of MIT and Harvard.
Dave BuntenDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Kenneth I BrewerIndependent Researcher.
Jenna TomkinsonDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Roshan KernDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Michael BornholdtImaging Platform, Broad Institute of MIT and Harvard.
Stephen FlemingData Sciences Platform, Broad Institute of MIT and Harvard.
Ruifan PeiImaging Platform, Broad Institute of MIT and Harvard.
John ArevaloImaging Platform, Broad Institute of MIT and Harvard.
Hillary TsangImaging Platform, Broad Institute of MIT and Harvard.
Vincent RubinettiDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Callum Tromans-CoiaImaging Platform, Broad Institute of MIT and Harvard.
Tim BeckerImaging Platform, Broad Institute of MIT and Harvard.
Erin WeisbartImaging Platform, Broad Institute of MIT and Harvard.
Charlotte BunneImaging Platform, Broad Institute of MIT and Harvard.
Alexandr A KalininImaging Platform, Broad Institute of MIT and Harvard.
Rebecca SenftImaging Platform, Broad Institute of MIT and Harvard.
Stephen J TaylorDepartment of Biomedical Informatics, University of Colorado School of Medicine.
Nasim JamaliImaging Platform, Broad Institute of MIT and Harvard.
Adeniyi AdeboyeImaging Platform, Broad Institute of MIT and Harvard.
Hamdah Shafqat AbbasiImaging Platform, Broad Institute of MIT and Harvard.
Allen GoodmanImaging Platform, Broad Institute of MIT and Harvard.
Juan C CaicedoImaging Platform, Broad Institute of MIT and Harvard.
Anne E CarpenterImaging Platform, Broad Institute of MIT and Harvard.
Beth A CiminiImaging Platform, Broad Institute of MIT and Harvard.
Shantanu SinghImaging Platform, Broad Institute of MIT and Harvard.
Gregory P WayDepartment of Biomedical Informatics, University of Colorado School of Medicine.

Funding

Computational Bioscience Program Training GrantT15LM009451 · NLM · UNIVERSITY OF COLORADO DENVER · PI Katherina Kechris-Mays, Arjun Krishnan · 2007 to 2026
$11.7M
Extracting rich information from biological imagesR35GM122547 · NIGMS · BROAD INSTITUTE, INC. · PI Anne E. Carpenter · 2017 to 2026
$6.2M
NIGMS NIH HHS R35 GM122547NLM NIH HHS T15 LM009451
6 · The paper itself

Abstract

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as "image-based profiling". We demonstrate Pycytominer's usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.

Identifiers

PMID38045474
PMCPMC10690292

What OpenQuestion holds

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Registered trials

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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.