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Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks

Weiguang Mao, Dennis Kostka, Maria Chikina
doi: https://doi.org/10.1101/219667
Weiguang Mao
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh
2Joint Carnegie Mellon - University of Pittsburgh Ph.D. Program in Computational Biology
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Dennis Kostka
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh
2Joint Carnegie Mellon - University of Pittsburgh Ph.D. Program in Computational Biology
3Department of Developmental Biology, School of Medicine, University of Pittsburgh
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Maria Chikina
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh
2Joint Carnegie Mellon - University of Pittsburgh Ph.D. Program in Computational Biology
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  • For correspondence: mchikina@pitt.edu
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Abstract

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 14, 2017.
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Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks
Weiguang Mao, Dennis Kostka, Maria Chikina
bioRxiv 219667; doi: https://doi.org/10.1101/219667
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Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks
Weiguang Mao, Dennis Kostka, Maria Chikina
bioRxiv 219667; doi: https://doi.org/10.1101/219667

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