RT Journal Article
SR Electronic
T1 Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 219667
DO 10.1101/219667
A1 Mao, Weiguang
A1 Kostka, Dennis
A1 Chikina, Maria
YR 2017
UL http://biorxiv.org/content/early/2017/11/14/219667.abstract
AB Background Gene regulatory sequences play critical roles in ensuring tightly controlled RNA expression patterns that are essential in a large variety of biological processes. Specifically, enhancer sequences drive expression of their target genes, and the availability of genome-wide maps of enhancer-promoter interactions has opened up the possibility to use machine learning approaches to extract and interpret features that define these interactions in different biological contexts.Methods Inspired by machine translation models we develop an attention-based neural network model, EPIANN, to predict enhancer-promoter interactions based on DNA sequences. Codes and data are available at https://github.com/wgmao/EPIANN.Results Our approach accurately predicts enhancer-promoter interactions across six cell lines. In addition, our method generates pairwise attention scores at the sequence level, which specify how short regions in the enhancer and promoter pair-up to drive the interaction prediction. This allows us to identify over-represented transcription factors (TF) binding sites and TF-pair interactions in the context of enhancer function.