Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

Our study shows that the VAE provides a valuable tool for advancing our understanding of cardiac function and its genetic underpinnings, outperforming traditional approaches in genetic and phenotypic discovery.

Authors

Institutions

Publication Details

Journal
Genome Medicine
Published
2025-10-09
DOI
https://doi.org/10.1186/s13073-025-01510-z
Citations
1
Primary Topic
Cardiac electrophysiology and arrhythmias
Type
article
Field-Weighted Citation Impact
1.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

Prisca K. Thami, Konstantinos Patlatzoglou, Libor Pastika, Sean L. Zheng et al.
1 citations
Genome Medicine
Cardiac electrophysiology and arrhythmias
1.39
article

Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

Prisca K. Thami, Konstantinos Patlatzoglou, Libor Pastika, Sean L. Zheng, Pantazis Theotokis, Rachel Buchan, Bart Loeys, Ewa Sieliwończyk, Daniel B. Kramer, Nicholas S. Peters, Arunashis Sau, George D. Powell, Lara Curran, Fu Siong Ng, Jonathan W. Waks, Kathryn A. McGurk, Massimo Mangino, James S Ware
article en
1 citations

Abstract

Our study shows that the VAE provides a valuable tool for advancing our understanding of cardiac function and its genetic underpinnings, outperforming traditional approaches in genetic and phenotypic discovery.

Genome MedicineVol. 17(1)
Beth Israel Deaconess Medical Center (US), Harvard University (US), University of Antwerp (BE), Imperial College Healthcare NHS Trust (GB), King's College London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), Chelsea and Westminster Hospital NHS Foundation Trust (GB), Royal Brompton & Harefield NHS Foundation Trust (GB), Harefield Hospital (GB), Lung Institute (US), Antwerp University Hospital (BE), MRC London Institute of Medical Sciences (GB), Imperial College London (GB)
British Heart Foundation, Medical Research Council
Openalex Percentile: Top 14%
Cardiac electrophysiology and arrhythmias
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.