Evidence map›Paper›PMID 39650478›Full record

ArticlePeerJ. Computer science2024

Genome-wide association studies of ischemic stroke based on interpretable machine learning.

Stefan Nikolić, Dmitry I Ignatov, Gennady V Khvorykh, Svetlana A Limborska, Andrey V Khrunin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026
    Review
  2. Review
  3. Article
  4. 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

5 authors.

Stefan NikolićLaboratory for Models and Methods of Computational Pragmatics; Department of Data Analysis and Artificial Intelligence, HSE University, Moscow, Russia.
Dmitry I IgnatovLaboratory for Models and Methods of Computational Pragmatics; Department of Data Analysis and Artificial Intelligence, HSE University, Moscow, Russia.ORCID 0000-0002-6584-8534
Gennady V KhvorykhNational Research Centre "Kurchatov Institute", Moscow, Russia.ORCID 0000-0001-8927-5921
Svetlana A LimborskaNational Research Centre "Kurchatov Institute", Moscow, Russia.
Andrey V KhruninNational Research Centre "Kurchatov Institute", Moscow, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS.

Indexed as

Genome-wide association studiesIlluminating druggable genomeInterpretable machine learningInterpretable neural network TabNetIschemic strokeSNP importanceSNP rankingXGBoost

Identifiers

PMID39650478
PMCPMC11623107

What OpenQuestion holds

Textmetadata
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.