Transcriptional enhancers play critical roles in regulation of gene expression, 
but their identification in the eukaryotic genome has been challenging. 
Recently, it was shown that enhancers in the mammalian genome are associated 
with characteristic histone modification patterns, which have been increasingly 
exploited for enhancer identification. However, only a limited number of cell 
types or chromatin marks have previously been investigated for this purpose, 
leaving the question unanswered whether there exists an optimal set of histone 
modifications for enhancer prediction in different cell types. Here, we address 
this issue by exploring genome-wide profiles of 24 histone modifications in two 
distinct human cell types, embryonic stem cells and lung fibroblasts. We 
developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer 
identification from Chromatin States) to integrate histone modification profiles 
for identification of enhancers, and used it to identify enhancers in a number 
of cell-types. We show that RFECS not only leads to more accurate and precise 
prediction of enhancers than previous methods, but also helps identify the most 
informative and robust set of three chromatin marks for enhancer prediction.