Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation.
Rodrigo Haas BuenoMariana Recamonde-MendozaPublished in: Molecular diagnosis & therapy (2020)
Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.
Keyphrases
- systematic review
- single cell
- atrial fibrillation
- meta analyses
- rna seq
- electronic health record
- genome wide
- poor prognosis
- big data
- bioinformatics analysis
- heart failure
- case control
- left atrial
- oral anticoagulants
- machine learning
- data analysis
- direct oral anticoagulants
- dna methylation
- binding protein
- long non coding rna
- acute coronary syndrome
- venous thromboembolism
- network analysis
- mitral valve
- deep learning