Abstract: Nucleosome localization refers to the position of DNA double helix relative to histone,and plays an important regulatory role in DNA transcription.It takes a lot of time and resources to detect nucleosome localization by biological experiments.Therefore,it is an important research direction to predict nucleosome localization by using DNA sequences based on computationalmethods.Aiming at the shortcomings of single model and single code in DNA sequence feature representation and learning in nucleosome location prediction,this paper proposes an end-to-end ensemble deep learning model FuseENup,which uses three coding methods to represent DNA data from multiple dimensions.Different models extract the key features hidden in the data from different dimensions,and construct a new DNA sequence representation model.Performing 20-fold cross-validation on the four data sets,compared to the current model CORENup with the best comprehensive performance for the nucleosome localization prediction problem,the accuracy and precision of FuseENup are improved by 3% and 9% on the HS data set,increases 2% and 6% on the DM data set,1% and 4% on the E data set.Compared with other machine learning and deep learning benchmark models,FuseENup has better performance.Experiments show that FuseENup can improve the prediction accuracy of nucleosomes localization,which shows the effectiveness and scientificity of the method.

Key words: Cross-validation, Deep learning, DNA sequence coding, Ensemble learning method, Nucleosome localization