A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson's disease.

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.

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.

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

1. Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.Genetic epidemiology · 2026
   Article
2. AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
   Article
3. Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.medRxiv : the preprint server for health sciences · 2024
   Article

Corrections and comments

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Authors and funding

2 authors at 1 institution in 1 country.

Funding

Abstract

PubMed holds no abstract for this paper.

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