Exploiting XG Boost For Predicting Enhancer-Promoter Interactions

(E-pub Ahead of Print)

Author(s): Xiaojuan Yu, Jianguo Zhou, Mingming Zhao, Chao Yi, Qing Duan, Wei Zhou*, Jin Li.

Journal Name: Current Bioinformatics

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Abstract:

Gene expression and disease control are regulated by the interaction between distal enhancers and proximal promoters, and the study of enhancer promoter interactions (EPIs) provides insight into the genetic basis of diseases. Although the recent emergence of high-throughput sequencing methods has deepened the understanding of EPIs, accurate prediction of EPIs still has limitations. We have implemented a XGBoost-based approach and introduced two sets of features (epigenomic and sequence) to predict the interactions between enhancers and promoters in different cell lines. Extensive experimental results show that XGBoost outperforms other methods and effectively predicts EPIs across three cell lines, especially when using epigenomic and sequence features.

Keywords: Enhancer-promoter interactions, Supervised learning, Machine learning, Gene expression, Feature extraction, XGBoost

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893615666200120103948
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