We developed a novel computational method called EPIP to reliably predict EPIs, especially condition-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 eight cell lines, EPIP reliably identifies EPIs, with an average area under the receiver operating characteristic curve of 0.95 and an average area under the precision–recall curve of 0.73. Tested on condition-specific EPIPs, EPIP correctly identified 99.26% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy.


The source code of EPIP is available here.


The manual for running the program is available here.


Amlan Talukder1, Samaneh Saadat1, Xiaoman Li2 and Haiyan Hu1

1Department of Electrical Engineering and Computer Science, University Of Central Florida, Orlando, FL 32826, USA.

2Burnett School of Biomedical Science, University Of Central Florida, Orlando, FL 32826, USA.

Related publication

Amlan Talukder, Samaneh Saadat, Xiaoman Li, Haiyan Hu (2018) EPIP: a novel approach for condition-specific enhancer–promoter interaction prediction Bioinformatics, 35, 3877–3883 (link)