Prediction of enhancer-promoter interaction based on ResNeXt

Abstract

Aiming at the low accuracy of traditional algorithms in predicting Enhancer-Promoter Interactions (EPIs) in the human genome, an EPIs prediction network based on ResNeXt and attention mechanism was proposed. In the data processing stage, the gene sequence data of a small number of positive samples in the data set is expanded to be consistent with the number of negative samples; then an EPIRNX model is constructed for feature selection and extraction for a given gene sequence, and long-distance features are mined for use in This cell line prediction; the transfer learning model EPIRNXTransfer was also trained for cross cell line prediction. Using AUROC and AUPRC as evaluation indicators, EPIRNX can better predict EPIs in this cell line than traditional models, and EPIRNX-Transfer can better predict EPIs across cell lines.