A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data

Zhong Zhuang, Xiaotong Shen, Wei Pan

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Abstract

Motivation: Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease-or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance. Results: First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance.

Original languageEnglish (US)
Pages (from-to)2899-2906
Number of pages8
JournalBioinformatics
Volume35
Issue number17
DOIs
StatePublished - Sep 1 2019

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