Evidence map›Paper›PMID 41926577›Full record

ArticleScience (New York, N.Y.)2026

DefensePredictor: A machine learning model to discover prokaryotic immune systems.

Peter C DeWeirdt, Emily M Mahoney, Michael T Laub

Abstract read
In one paragraph

Article in Science (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. How bacterial immune systems sense phage infection.Nature reviews. Microbiology · 2026
    Review
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  4. Article
  5. Article
  6. Review
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  8. Article
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  14. Article
  15. Article
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  17. Article
  18. A methylome-derived mbioRxiv : the preprint server for biology · 2026
    Article
  19. The uncharacterizedmicroPublication biology · 2026
    Article
  20. 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

3 authors.

Peter C DeWeirdtDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-4707-7940
Emily M MahoneyDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0009-0006-4862-5028
Michael T LaubDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-8288-7607

Funding

Systematic identification of novel anti-phage defense mechanisms in the E. coli pangenomeR21AI181878 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LAUB, MICHAEL · 2024 to 2025
$431k
Howard Hughes Medical InstituteNIAID NIH HHS R21 AI181878
6 · The paper itself

Abstract

Antiphage defense systems protect bacteria from viral infection and have inspired important biotechnologies such as CRISPR-Cas9 while also revealing the evolutionary roots of eukaryotic innate immunity. Many systems have been discovered by genomic colocalization, but this approach cannot identify systems outside of defense islands. We present DefensePredictor, a machine learning model that uses protein language model embeddings to classify proteins as defensive. Applying DefensePredictor to 69 diverse

Indexed as

BacteriophagesEscherichia coliEscherichia coli ProteinsImmunity, InnateMachine LearningGenome, BacterialEscherichia coli Proteins

Identifiers

PMID41926577
PMCPMC13092281

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.