Disentangling factors affecting bacterial transcriptional regulatory network inference.

Gaoyuan Li, Joshua T Burrows, Xuwen A Lou, Bernhard O Palsson, Daniel C Zielinski

Abstract read

In one paragraph

Article in iScience, 2026. 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

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

5 authors.

Gaoyuan LiDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Joshua T BurrowsDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Xuwen A LouDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Bernhard O PalssonDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Daniel C ZielinskiDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid growth of bacterial gene expression databases has enabled computational inference of transcriptional regulatory networks (TRNs), yet it remains unclear why mathematically simple models often capture their apparent complexity. Using a 1035-sample

Indexed as

machine learningtranscriptional regulationTRN inference

Identifiers

PMID42502393

DOI10.1016/j.isci.2026.116834

PMCPMC13401006

What Socratic holds

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LicenceCC BY

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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 Socratic graph.