iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition

“…Obviously, Enhancer-LSTMAtt achieved competitive performance with these state-of-the-art methods. In the first stage, Enhancer-LSTMAtt reached the best SP (0.8150), the best ACC (0.8050), and the best MCC (0.6101), achieved a second AUC (0.8588), which was less than the AUC of iEnhancer-RF, and obtained a competitive SN (0.7950), which was less than the SN of iEnhancer-GAN [ 60 ], spEnhancer [ 58 ], iEnhancer-5Step [ 47 ], piEnPred [ 61 ], iEnhancer-RD [ 62 ], and iEnhancer-BERT [ 55 ]. In the second stage, the Enhancer-LSTMAtt reached the best SN, ACC and MCC, a second AUC to that of the iEnhancer-RF [ 57 ], and a second SP to that of the Enhancer-DRRNN [ 54 ].…”

“…For fair comparison with the state-of-the-art methods, we used the same benchmark dataset as those in iEnhancer-2L [ 40 ], iEnhancer-PsedeKNC [ 41 ], EnhancerPred [ 42 ], EnhancerPred2.0 [ 43 ], Enhancer-Tri-N [ 44 ], iEnhaner-2L-Hybrid [ 45 ], iEnhancer-EL [ 46 ], iEnhancer-5Step [ 47 ], DeployEnhancer [ 48 ], ES-ARCNN [ 49 ], iEnhancer-ECNN [ 50 ], EnhancerP-2L [ 51 ], iEnhancer-CNN [ 52 ], iEnhancer-XG [ 53 ], Enhancer-DRRNN [ 54 ], Enhancer-BERT [ 55 ], iEnhancer-KL [ 56 ], iEnhancer-RF [ 57 ], spEnhancer [ 58 ], iEnhancer-EBLSTM [ 59 ], iEnhancer-GAN [ 60 ], piEnPred [ 61 ], iEnhancer-RD [ 62 ], and iEnhancer-MFGBDT [ 63 ]. The dataset was initially collected by Liu et al [ 40 ] from chromatin state information of nine cell lines (H1ES, K562,GM12878, HepG2, HUVEC, HSMM, NHLF, NHEK and HME) which was annotated by ChromHMM [ 69 , 70 ].…”

“…Frist Stage iEnhancer-PsedeKNC [41] 0.7731 0.7630 0.7678 0.5400 0.8500 iEnhancer-5Step [47] 0.8110 0.8350 0.8230 0.6500 -DeployEnhancer [48] 0.7325 0.7642 0.7483 0.4980 0.7694 EnhancerP-2L [51] 0.9077 0.9259 0.9168 0.8340 0.9400 iEnhancer-CNN [52] 0.7588 0.8888 0.8063 0.6929 0.8957 Enhancer-BERT [55] 0.7950 0.7300 0.7620 0.5250 -iEnhancer-KL [56] 0.8322 0.8524 0.8423 0.6800 -iEnhancer-RF [57] 0.7364 0.7871 0.7618 0.5264 0.8400 piEnPred [61] 0.9228 0.8047 0.8788 0.7660 0.9603 iEnhancer-RD [62] 0 Table 6 lists evaluation of performances of all of the 19 methods on the independent test. To the best of our knowledge, nearly all of the methods used the same independent dataset S i for independent test, and no other published enhancers were collected as the second independent dataset.…”

“…Furthermore, Machine learning-based methods are capable of discovering non-linear hidden motifs of enhancers. To date, there are at least twenty machine learning-based methods for enhancer prediction [ 16 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ], such as iEnhancer-2L [ 40 ], iEnhancer-PsedeKNC [ 41 ], EnhancerPred [ 42 ], and EnhancerPred2.0 [ 43 ]. The general workflow of these methods is firstly to compute representation of sequences such as pseudo k-tuple nucleotide composition, nucleotide binary profiles, as well as accumulated nucleotide frequency, then to learn a classifier by using a machine learning algorithm such as support vector machine and random forest, and finally to predict unknown sequences.…”

Analysis of the landscape of human enhancer sequences in biological databases

“…On the other hand, novel software to identify enhancer sequences is being developed [173] , [174] . Comparative studies of algorithms and revisions about these tools have been previously elaborated in other works [130] , [175] , [176] , although a more recent in-depth review regarding this issue would be of interest.…”