ArticleFrontiers in genetics2021
A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer's Disease.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- Unsupervised machine learning integrates genomic variants and EMR to unravel mechanisms of brain hemorrhage and epilepsy as early indicators of Alzheimer's in down syndrome.Alzheimer's research & therapy · 2025Article
- AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease.iScience · 2024Article
- Artificial intelligence in Immuno-genetics.Bioinformation · 2024Review
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia.Journal of biomedical informatics · 2023Article
- Novel approaches to increase synaptic resilience as potential treatments for Alzheimer's disease.Seminars in cell & developmental biology · 2023Review
- Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets.Scientific reports · 2023Review
- Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
There is hope that genomic information will assist prediction, treatment, and understanding of Alzheimer's disease (AD). Here, using exome data from ∼10,000 individuals, we explore machine learning neural network (NN) methods to estimate the impact of SNPs (i.e., genetic variants) on AD risk. We develop an NN-based method (netSNP) that identifies hundreds of novel potentially protective or at-risk AD-associated SNPs (along with an effect measure); the majority with frequency under 0.01. For case individuals, the number of "protective" (or "at-risk") netSNP-identified SNPs in their genome correlates positively (or inversely) with their age of AD diagnosis and inversely (or positively) with autopsy neuropathology. The effect measure increases correlations. Simulations suggest our results are not due to genetic linkage, overfitting, or bias introduced by netSNP. These findings suggest that netSNP can identify SNPs associated with AD pathophysiology that may assist with the diagnosis and mechanistic understanding of the disease.
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Registered trials
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.