Evidence map›Paper›PMID 41799017›Full record

ArticleNAR genomics and bioinformatics2026

Interpreting artificial neural networks to detect genome-wide association signals for complex traits.

Burak Yelmen, Maris Alver, Merve Nur Güler, Estonian Biobank Research Team, Flora Jay, Lili Milani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Burak YelmenEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010,Estonia.ORCID https://orcid.org/0000-0003-0731-0223
Maris AlverEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010,Estonia.
Merve Nur GülerEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010,Estonia.
Estonian Biobank Research Team
Flora JayCNRS, INRIA, LISN, Paris-Saclay University, Orsay, 91190,France.
Lili MilaniEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010,Estonia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial interplay of genomic and environmental influences. Although GWAS have identified thousands of variants for multiple complex traits, conventional statistical approaches can be limited by simplified assumptions such as linearity and lack of epistasis. In this work, we trained artificial neural networks using genome-wide genotype data to predict simulated and real complex traits. We extracted feature importance scores via different post hoc interpretability methods to identify potentially associated locus/loci (PAL) for the target phenotype and devised an approach for estimating

Indexed as

Genome-Wide Association StudyNeural Networks, ComputerBipolar DisorderGenotypeHumansPhenotypePolymorphism, Single NucleotideSchizophrenia

Identifiers

PMID41799017
PMCPMC12964191

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.