Transcription regulation in multicellular eukaryotes is orchestrated by a number 
of DNA functional elements located at gene regulatory regions. Some regulatory 
regions (e.g. enhancers) are located far away from the gene they affect. 
Identification of distal regulatory elements is a challenge for the 
bioinformatics research. Although existing methodologies increased the number of 
computationally predicted enhancers, performance inconsistency of computational 
models across different cell-lines, class imbalance within the learning sets and 
ad hoc rules for selecting enhancer candidates for supervised learning, are some 
key questions that require further examination. In this study we developed DEEP, 
a novel ensemble prediction framework. DEEP integrates three components with 
diverse characteristics that streamline the analysis of enhancer's properties in 
a great variety of cellular conditions. In our method we train many individual 
classification models that we combine to classify DNA regions as enhancers or 
non-enhancers. DEEP uses features derived from histone modification marks or 
attributes coming from sequence characteristics. Experimental results indicate 
that DEEP performs better than four state-of-the-art methods on the ENCODE data. 
We report the first computational enhancer prediction results on FANTOM5 data 
where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity 
and sensitivity across 36 different tissues. We further present results derived 
using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested 
on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP 
framework is publicly available at http://cbrc.kaust.edu.sa/deep/.