Qinhu Zhang

1,5 1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo 315201, China; 2Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning, 530007, China; 3School of Information Engineering, Xuzhou University of Technology, Xuzhou 221018, China; 4Faculty of Data Science, City University of Macau, Macau 999078, China; 5Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University, Shanghai 200092, China Corresponding author:...

1 Introduction Transcription factors (TFs) play an essential role in transcriptional regulatory networks by binding to non-coding functional regions at specific locations to promote or repress the transcription of target genes.[1] GWAS (Genome-wide association study) has revealed that the vast majority of mutations in the human genome exist in non-coding functional regions, and some of them may directly disrupt DNA binding affinities of TFs, sequentially altering the regulation of gene...

Abstract Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, we first propose a novel Nucleotide-Level Deep Neural Network (NLDNN) to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task.