ArticleCells2021
Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.
Article in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 19 citations in OpenAlex.
- Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease.Gut microbes · 2025Article
- Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease.Biomarker research · 2025Article
- RASGEF1C methylation for the distinguishment and classification of benign and malignant thyroid tumors.Clinical epigenetics · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Amyloid-beta metabolism in age-related neurocardiovascular diseases.European heart journal · 2025Review
- Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.Physiological reports · 2025Review
- Analysis and prediction of research hotspots and trends in heart failure research.Journal of translational internal medicine · 2024Article
- Genetic susceptibility to severe COVID-19.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2023Review
- Electrical Remodelling in Cardiac Disease.Cells · 2023Article
- Actin Up: An Overview of the Rac GEF Dock1/Dock180 and Its Role in Cytoskeleton Rearrangement.Cells · 2022Review
- Discovery of a Biomarker Signature That Reveals a Molecular Mechanism Underlying Diabetic Kidney Disease via Organ Cross Talk.Diabetes care · 2022Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.
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Registered trials
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