Evidence map›Paper›PMID 29109402›Full record

ArticleScientific reports2017

Machine learning model for sequence-driven DNA G-quadruplex formation.

Aleksandr B Sahakyan, Vicki S Chambers, Giovanni Marsico, Tobias Santner, Marco Di Antonio, Shankar Balasubramanian

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 2 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

95 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  20. The complete genome of a songbird.bioRxiv : the preprint server for biology · 2025
    Article

35 more citing papers are in PubMed but not listed here.

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

6 authors.

Aleksandr B SahakyanDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Vicki S ChambersDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Giovanni MarsicoDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Tobias SantnerDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Marco Di AntonioDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Shankar BalasubramanianDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. sb10031@cam.ac.uk.ORCID 0000-0002-0281-5815

Funding

Cancer Research UK 18618Cancer Research UK C14303/A17197Cancer Research UK C9681/A18618European Research Council 339778Wellcome TrustWellcome Trust 099232/Z/12/Z
6 · The paper itself

Abstract

We describe a sequence-based computational model to predict DNA G-quadruplex (G4) formation. The model was developed using large-scale machine learning from an extensive experimental G4-formation dataset, recently obtained for the human genome via G4-seq methodology. Our model differentiates many widely accepted putative quadruplex sequences that do not actually form stable genomic G4 structures, correctly assessing the G4 folding potential of over 700,000 such sequences in the human genome. Moreover, our approach reveals the relative importance of sequence-based features coming from both within the G4 motifs and their flanking regions. The developed model can be applied to any DNA sequence or genome to characterise sequence-driven intramolecular G4 formation propensities.

Indexed as

G-QuadruplexesMachine LearningBase SequenceComputer SimulationGenome, HumanHumans

Identifiers

PMID29109402
PMCPMC5673958

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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.