| ANN | Artificial neural network |
| ATAC-seq | Assay for transposase-accessible chromatin using sequencing |
| auROC | Area under the receiver operating characteristic |
| BRNN | Bidirectional recurrent neural networks |
| ChIA-PET | Chromatin interaction analysis with paired-end tag |
| ChIP | Chromatin immuno precipitation |
| CNN | Convolutional neural network |
| CRM | cis-regulatory module |
| CSI-ANN | Chromatin signature identification by artificial neural network |
| DECRES | Deep learning for identifying cis-regulatory elements |
| ECNN | Ensemble of CNN |
| ENCODE | Encyclopedia of DNA elements |
| FAIRE | Formaldehyde-assisted isolation of regulatory elements |
| FANTOM | Functional annotation of the mammalian genome |
| Gkm-SVM | Gapped kmer support vector machine |
| HMM | Hidden Markov model |
| Kmer-SVM | kmer-support vector machine |
| ML | Machine learning |
| REDfly | Regulatory element database for Drosophila |
| RF | Random forest |
| RFECS | Random forest-based enhancer identification using chromatin states |
| ROC | Receiver operating characteristic |
| scATAC-seq | Single cell assay for transposase-accessible chromatin using sequencing |
| SCRMshaw | Supervised cis-regulatory module discovery |
| STARR-seq | Self-transcribing active regulatory region sequencing |
| SVM | Support vector machine |
| TF | Transcription factor |
| TFBS | Transcription factor binding site |