Exploiting XG Boost for Predicting Enhancer-promoter Interactions

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

Journal Name: Current Bioinformatics

Volume 15 , Issue 9 , 2020


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: 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.

Objective: Although the recent emergence of high-throughput sequencing methods have a deepened understanding of EPIs, accurate prediction of EPIs still limitations.

Methods: 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.

Results: Extensive experimental results show that XGBoost effectively predicts EPIs across three cell lines, especially when using epigenomic and sequence features.

Conclusion: XGBoost outperforms other methods, such as random forest, Adadboost, GBDT, and TargetFinder.

Keywords: Enhancer-promoter interactions, supervised learning, machine learning, gene expression, feature extraction, XGBoost.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 9
Year: 2020
Page: [1036 - 1045]
Pages: 10
DOI: 10.2174/1574893615666200120103948
Price: $65

Article Metrics

PDF: 17