E3S Web Conf.
Volume 218, 20202020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
|Number of page(s)||5|
|Section||Environmental Chemistry and Environmental Pollution Analysis and Control|
|Published online||11 December 2020|
Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions
School of Software, Yunnan University, Kunming Yunnan China Equal contributor
2 School of Information Science and Engineering, Yunnan University, Kunming Yunnan China Equal contributor
3 School of Software, Yunnan University, Kunming Yunnan China
4 School of Software, Yunnan University, Kunming Yunnan China
5 School of Software, Yunnan University, Kunming Yunnan China
How to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of many machine learning approaches has been able to predict enhancer-promoter interactions (EPIs), global and precise prediction of EPIs at the genomic level still requires further exploration.In this paper, we develop an integrated EPIs prediction method, called EpPredictor with improved performance. By using various features of histone modifications, transcription factor binding sites, and DNA sequences among the human genome, a robust supervised machine learning algorithm, named LightGBM, is introduced to predict enhancer-promoter interactions (EPIs). Among six different cell lines, our method effectively predicts the enhancer-promoter interactions (EPIs) and achieves better performance in F1-score and AUC compared to other methods, such as TargetFinder and PEP.
© The Authors, published by EDP Sciences, 2020
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