We present EP-DNN, a protocol for predicting enhancers based on chromatin 
features, in different cell types. Specifically, we use a deep neural network 
(DNN)-based architecture to extract enhancer signatures in a representative 
human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). 
We train EP-DNN using p300 binding sites, as enhancers, and TSS and random 
non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions 
to quantify the validation rate and compare against two state-of-the-art 
methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a 
validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, 
for a given number of enhancer predictions and also scales better for a larger 
number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to 
be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer 
signature set and our EP-DNN model is expressive enough to extract these 
subtleties. Our work shows how to leverage the full expressivity of deep 
learning models, using multiple hidden layers, while avoiding overfitting on the 
training data. We also lay the foundation for exploration of cross-cell enhancer 
predictions, potentially reducing the need for expensive experimentation.