Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

Journal: BMC cardiovascular disorders

PMID: 33509101

Abstract

BACKGROUND: Atrial 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.

Authors

Yaozhong Liu

Department of Cardiovascular Medicine/Cardiac Catheterization Lab, Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan Province, People's Republic of China.
Fan Bai

School of Information Engineering, Chang'an University, Xi'an 710010, China.
Zhenwei Tang

Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Na Liu
Qiming Liu

Department of Cardiovascular Medicine/Cardiac Catheterization Lab, Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan Province, People's Republic of China. qimingliu@csu.edu.cn.

Keywords

Atrial Fibrillation Bayes Theorem Case-Control Studies Databases, Genetic Female Gene Expression Profiling Gene Regulatory Networks Genetic Association Studies Heart Atria Heart Valve Diseases Humans Machine Learning Male Middle Aged Oligonucleotide Array Sequence Analysis Protein Interaction Maps Proteomics Signal Transduction Transcriptome

External Resources

View on PubMed Access via DOI PubMed (33509101)

Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

Abstract

Authors

Keywords

External Resources

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