{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kwon OS"],"funding":["NIEHS NIH HHS","National Research Foundation of Korea","the Ministry of Health &amp; Welfare, Republic of Korea"],"pagination":["e001898"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8796259"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["<h4>Objective</h4>We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN).<h4>Methods</h4>This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed"],"journal":["Open heart"],"pubmed_title":["Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence."],"pmcid":["PMC8796259"],"funding_grant_id":["HI21C0011","NRF-2020R1A2B01001695","27398C0011","27305C0011","27307C0011","NRF-2019R1I1A1A01041440","HI19C0114"],"pubmed_authors":["Hong M","Lee MH","Choi EK","Lim HE","Pak HN","Oh S","Kwon OS","Yu HT","Shim J","Hwang I","Kim TH","Joung B","Kim YH","Uhm JS"],"additional_accession":[]},"is_claimable":false,"name":"Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence.","description":"<h4>Objective</h4>We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN).<h4>Methods</h4>This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2026-06-07T07:30:13.121Z","creation":"2025-04-04T07:52:06.318Z"},"accession":"S-EPMC8796259","cross_references":{"pubmed":["35086918"],"doi":["10.1136/openhrt-2021-001898"]}}