Key result
A 10-gene Naive Bayes classifier accurately distinguished atrial fibrillation from sinus rhythm in an independent validation set with a precision of 87.5% and an AUC of 0.995.
Why the study?
AF is the most common arrhythmia with poorly understood mechanisms.
Observational (n=178)
Yes
Effect estimate: AUC 0.995
An integrative multi-omics and machine learning approach identified 10 feature genes that accurately distinguish atrial fibrillation from sinus rhythm, providing potential novel diagnostic and therapeutic targets.
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Hypothesis-generating for transcriptomic AF detection; requires prospective validation before clinical consideration.
Liu et al. (2021) conducted an observational in Atrial fibrillation (n=178). Atrial fibrillation vs. Sinus rhythm was evaluated on Classification of AF from SR samples in the independent validation test set (AUC 0.995). A 10-gene Naive Bayes classifier accurately distinguished atrial fibrillation from sinus rhythm in an independent validation set with a precision of 87.5% and an AUC of 0.995.