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17%, and the tricks of data augmentation can effectively improve the prediction performance. We further tested ES-ARCNN on an independent dataset and obtained 65.5% accuracy, which has more than 4% improvement over the other three existing methods. The results in 10CV and independent tests show that ES-ARCNN can effectively predict the enhancer strength. The transcription factor binding sites (TFBSs) enrichment analysis shows that from the mechanistic perspective, enhancer strength is associated with a higher density of important TFBSs i
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