SynthesisBMC cardiovascular disorders2021
Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.
Synthesis in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.Frontiers in genetics · 2026Pooled it
- Evidence for a role of adverse sarcomere signaling in atrial fibrillation induction.Journal of molecular and cellular cardiology plus · 2026Review
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Proteome profiling reveals early diagnostic biomarker candidates for colorectal cancer.BMC cancer · 2026Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Unlocking the secrets of Cardiac development and function: the critical role of FHL2.Molecular and cellular biochemistry · 2025Review
- Distinct functional and molecular profiles between physiological and pathological atrial enlargement offer potential new therapeutic opportunities for atrial fibrillation.Clinical science (London, England : 1979) · 2024Article
- Zebrafish Congenital Heart Disease Models: Opportunities and Challenges.International journal of molecular sciences · 2024Review
- Modelling and targeting mechanical forces in organ fibrosis.Nature reviews bioengineering · 2024Article
- Atrial Proteomic Profiling Reveals a Switch Towards Profibrotic Gene Expression Program in CREM-IbΔC-X Mice with Persistent Atrial Fibrillation.bioRxiv : the preprint server for biology · 2024Article
- Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia.Journal of cardiovascular development and disease · 2023Review
- Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation.Biomedicines · 2023Article
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
- Alterations in cytoskeletal and CaFrontiers in cardiovascular medicine · 2023Article
- Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.Reviews in cardiovascular medicine · 2022Article
- Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-Omics Analysis of Gene and Protein Candidates Possibly Related to Tetrodotoxin Accumulation in the Skin ofMarine drugs · 2021Article
- Review
- A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAtrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.
methodsAt the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.
results863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.
conclusionTaken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.
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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.