Evidence map›Paper›PMID 31968357›Full record

ArticleThe Journal of cell biology2020

A reference library for assigning protein subcellular localizations by image-based machine learning.

Wiebke Schormann, Santosh Hariharan, David W Andrews

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

14 citing papers in PubMed, 22 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Wiebke SchormannBiological Sciences, Sunnybrook Research Institute, Toronto, Canada.
Santosh HariharanBiological Sciences, Sunnybrook Research Institute, Toronto, Canada.
David W AndrewsBiological Sciences, Sunnybrook Research Institute, Toronto, Canada.
Sunnybrook Hospital · CAUniversity of Toronto · CA

Funding

CIHR FDN 143312
6 · The paper itself

Abstract

Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morphology and statistical features were measured. Machine learning algorithms using these features permit automated assignment of the localization of other proteins and dyes in both cell types with very high accuracy. Automated assignment of subcellular localizations for model tail-anchored proteins with randomly mutated C-terminal targeting sequences allowed the discovery of motifs responsible for targeting to mitochondria, endoplasmic reticulum, and the late secretory pathway. Analysis of directed mutants enabled refinement of these motifs and characterization of protein distributions in within cellular subcompartments.

Indexed as

AnimalsCell LineEpithelial CellsGreen Fluorescent ProteinsHumansImage Processing, Computer-AssistedMachine LearningMiceMicroscopy, ConfocalMutationOrganellesPattern Recognition, AutomatedProtein TransportRecombinant Fusion ProteinsReference StandardsSecretory Pathwayenhanced green fluorescent proteinGreen Fluorescent ProteinsRecombinant Fusion Proteins

Identifiers

PMID31968357
PMCPMC7055006
OpenAlexW3001208427

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.