iPSI(2L)-EDL: a Two-layer Predictor for Identifying Promoters and their Types based on Ensemble Deep Learning - Xiao, Xuan; Hu, Zaihao; Luo, ZhenTao; Xu, Zhaochun (Current Bioinformatics, 2024)
Background: DNA fragments located near the transcription initiation site are categorizedinto two types, strong promoters, and weak promoters, based on their transcriptional activation andexpression levels.Identifying promoters and determining their strength is crucial for understanding gene expressionregulation. There is a need to improve the predictive quality of promoter prediction models for realworldapplications.Methods: The most recent training dataset was constructed from the RegalonDB website, where allpromoters had been experimentally validated, and their sequence similarity was below 85%. DNAsequence samples were represented using one-hot encoding, along with nucleotide chemical propertiesand density (NCPD). An integrated deep learning framework was developed, incorporating amulti-head attention module, a long short-term memory (LSTM) module, and a convolutional neuralnetwork (CNN) module.Results: The AUC and MCC for iPSI(2L)-EDL in identifying promoters improved by 2.23% and2.96%, respectively, compared to the PseDNC-DL method on independent testing data. The AUCand MCC for iPSI(2L)-EDL increased by 3.74% and 5.86%, respectively, in predicting promoterstrength type.Conclusion: The importance of different input positions and long-range dependency relationshipsamong features contributed to better promoter recognition. The CNN played a crucial role in recognizingpromoters. Furthermore, to facilitate access for most experimental scientists, a user-friendlyweb server has been established, which can be accessed at http://47.94.248.117/IPSW(2L)-EDL.
Keywords: ensemble deep learning