MOTIVATION: The identification of enhancer-promoter interactions (EPIs) especially cell-specific ones is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and the available computational methods either do not consider or have low-performance in predicting cell-specific EPIs. RESULTS: We developed a novel computational method called EPIP to reliably predict enhancer-promoter interactions, especially cell-specific ones. EPIP is capable of predicting interactions in samples with limited data as well as in samples with abundant data. Tested on more than 8 cell lines, EPIP reliably identifies enhancer-promoter interactions, with an average area under the receiver operating characteristic curve of 0.97, an average area under the precision-recall curve of 0.93, and an F-score of 0.77. Tested on 64945 cell-specific enhancer-promoter interactions, EPIP correctly identified 99.61% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy. AVAILABILITY: The EPIP tool is freely available at http://www.cs.ucf.edu/~xiaoman/EPIP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.