Transcriptional enhancers are non-coding segments of DNA that play a central 
role in the spatiotemporal regulation of gene expression programs. However, 
systematically and precisely predicting enhancers remain a major challenge. 
Although existing methods have achieved some success in enhancer prediction, 
they still suffer from many issues. We developed a deep learning-based 
algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which 
can directly learn an enhancer predictor from massively heterogeneous data and 
generalize in ways that are mostly consistent across various cell types/tissues. 
We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 
cells, and demonstrated that PEDLA framework integrates diverse heterogeneous 
features and gives state-of-the-art performance relative to five existing 
methods for enhancer prediction. We further extended PEDLA to iteratively learn 
from 22 training cell types/tissues. Our results showed that PEDLA manifested 
superior performance consistency in both training and independent test sets. On 
average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of 
sensitivity and specificity across 22 training cell types/tissues, as well as 
95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. 
Together, our work illustrates the power of harnessing state-of-the-art deep 
learning techniques to consistently identify regulatory elements at a 
genome-wide scale from massively heterogeneous data across diverse cell 
types/tissues.