Evidence map›Paper›PMID 40708668›Full record

ArticleRoyal Society open science2025

Machine learning reveals distinct gene expression signatures across tissue states in stony coral tissue loss disease.

Kelsey M Beavers, Daniela Gutierrez-Andrade, Emily W Van Buren, Madison A Emery, Marilyn E Brandt, Amy Apprill, Laura D Mydlarz

Abstract read
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In one paragraph

Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

7 authors.

Kelsey M BeaversThe University of Texas at Austin Texas Advanced Computing Center, Austin, TX, USA.ORCID https://orcid.org/0000-0003-3366-0366
Daniela Gutierrez-AndradeDepartment of Biology, The University of Texas at Arlington, Arlington, TX, USA.ORCID https://orcid.org/0009-0006-6911-2539
Emily W Van BurenDepartment of Biology, The University of Texas at Arlington, Arlington, TX, USA.ORCID https://orcid.org/0009-0002-0415-5934
Madison A EmeryDepartment of Biology, The University of Texas at Arlington, Arlington, TX, USA.ORCID https://orcid.org/0009-0000-2590-1110
Marilyn E BrandtCenter for Marine and Environmental Studies, University of the Virgin Islands, St Thomas, VI, USA.ORCID https://orcid.org/0000-0001-8639-7851
Amy ApprillMarine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA.ORCID https://orcid.org/0000-0002-4249-2977
Laura D MydlarzDepartment of Biology, The University of Texas at Arlington, Arlington, TX, USA.ORCID https://orcid.org/0000-0002-8371-0766

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stony coral tissue loss disease (SCTLD) has rapidly degraded Caribbean reefs, compounding climate-related stressors and threatening ecosystem stability. Effective intervention requires understanding the mechanisms driving disease progression and resistance. Here, we apply a supervised machine learning approach-support vector machine recursive feature elimination-combined with differential gene expression analysis to describe SCTLD in the reef-building coral

Indexed as

coralgene expressionmachine learningstony coral tissue loss diseasesymbiosistranscriptomics

Identifiers

PMID40708668
PMCPMC12289216

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.