Evidence map›Paper›PMID 41726969›Full record

ArticlebioRxiv : the preprint server for biology2026

Existence of Causation without Correlation in Transcriptional Networks.

Gerald M Pao, Ethan R Deyle, Hao Ye, Junko Ogawa, Marisela Guaderrama, Manching Ku, Tom Lorimer, Nina Tonnu, Erik Saberski, Joseph Park and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

14 authors.

Gerald M PaoOkinawa Institute of Science and Technology Graduate University, Biological Nonlinear Dynamics Data Science Unit, Okinawa 904-0495 Japan.ORCID 0000-0001-8719-2540
Ethan R DeyleScripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0202.ORCID 0000-0001-8704-8434
Hao YeScripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0202.
Junko OgawaOkinawa Institute of Science and Technology Graduate University, Biological Nonlinear Dynamics Data Science Unit, Okinawa 904-0495 Japan.
Marisela GuaderramaUniversity of California San Diego, Department of Pediatrics, La Jolla, CA 92093.
Manching KuMammoth Biosciences, 1000 Marina Blvd, Brisbane, CA 94005.
Tom LorimerScripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0202.
Nina TonnuSalk Institute for Biological studies, La Jolla, California 92037.
Erik SaberskiScripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0202.
Joseph ParkOkinawa Institute of Science and Technology Graduate University, Biological Nonlinear Dynamics Data Science Unit, Okinawa 904-0495 Japan.
Eugene KeDepartment of Microbiology, Immunology, and Cancer, School of Medicine, University of Virginia, Charlottesville, VA 22903.
Curt WittenbergDepartment of Cell and Molecular Biology, The Scripps Research Institute, La Jolla, California, 92037.
Inder M VermaSalk Institute for Biological studies, La Jolla, California 92037.
George SugiharaScripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0202.

Funding

Viral Vector Core (VVC)P30CA014195 · NCI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI Reuben Shaw · 1985 to 2026
$82.8M
REGULATION OF G1-SPECIFIC GENE EXPRESSION IN YEASTR01GM059441 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI WITTENBERG, CURT · 2000 to 2016
$6.9M
NCI NIH HHS P30 CA014195NIGMS NIH HHS R01 GM059441
6 · The paper itself

Abstract

It is commonly assumed that lack of correlation is evidence for lack of causal relationship. Here, however we show that in transcriptional networks, causal linkages can exist in the absence of correlation. We find that a substantial proportion of transcribed genes in yeast and in mouse, show evidence of state-dependent (nonlinear) and temporally coordinated dynamics in their expression patterns (65-77%). Using a test that accommodates this fact, we uncover strong causal relationships that are invisible to correlation-based analyses for both yeast and mouse models. Specifically, for yeast we detect uncorrelated causal relationships for the transcriptional regulators

Identifiers

PMID41726969
PMCPMC12918784

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.