Super-enhancers (SEs) are clusters of transcriptional enhancers which control 
the expression of cell identity and disease-associated genes. Current studies 
demonstrated the role of multiple factors in SE formation; however, a systematic 
analysis to assess the relative predictive importance of chromatin and sequence 
features of SEs and their constituents is lacking. In addition, a predictive 
model that integrates various types of data to predict SEs has not been 
established. Here, we integrated diverse types of genomic and epigenomic 
datasets to identify key signatures of SEs and investigated their predictive 
importance. Through integrative modeling, we found Cdk8, Cdk9, and Smad3 as new 
features of SEs, which can define known and new SEs in mouse embryonic stem 
cells and pro-B cells. We compared six state-of-the-art machine learning models 
to predict SEs and showed that non-parametric ensemble models performed better 
as compared to parametric. We validated these models using cross-validation and 
also independent datasets in four human cell-types. Taken together, our 
systematic analysis and ranking of features can be used as a platform to define 
and understand the biology of SEs in other cell-types.