Evidence map›Paper›PMID 34777307›Full record

ArticleFrontiers in microbiology2021

Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile

Siddharth M Chauhan, Saugat Poudel, Kevin Rychel, Cameron Lamoureux, Reo Yoo, Tahani Al Bulushi, Yuan Yuan, Bernhard O Palsson, Anand V Sastry

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  9. Machine learning reveals the transcriptional regulatory network and circadian dynamics ofProceedings of the National Academy of Sciences of the United States of America · 2024
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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

9 authors.

Siddharth M ChauhanDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Saugat PoudelDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Kevin RychelDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Cameron LamoureuxDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Reo YooDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Tahani Al BulushiDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Yuan YuanDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Bernhard O PalssonDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Anand V SastryDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality

Indexed as

archaeaindependent component analysis (ICA)machine learningregulon discoverysystems biologythermophiletranscriptional regulationtranscriptional regulatory network (TRN)

Identifiers

PMID34777307
PMCPMC8578740

What OpenQuestion holds

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LicenceCC BY
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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.