iEnhancer-RF: Identifying enhancers and their strength by enhanced feature representation using random forest

Lim, Dae Yeong; Khanal, Jhabindra; Tayara, Hilal; Chong, Kil To

doi:10.1016/j.chemolab.2021.104284

Cited by 27 publications

(26 citation statements)

References 65 publications

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“…In this case, the overall indices could be used, such as ACC and MCC. In the 5-fold cross-validation, Enhancer-LSTMAtt was superior to Enhancer-BERT [ 55 ], DeployEnhancer [ 48 ] and iEnhancer-RF [ 57 ] in terms of ACC and MCC in the first stage and exceeded iEnhancer-PsedeKNC [ 41 ], DeployEnhancer [ 48 ], EnhancerP-2L [ 51 ], and iEnhancer-RF [ 57 ] in terms of MCC in the second stage. In the 10-fold cross-validation, Enhancer-LSTMAtt reached competitive performance with ES-ARCNN [ 49 ], iEnhancer-XG [ 53 ], and iEnhancer-MFGBDT [ 63 ] in the second stage.…”

Section: Resultsmentioning

confidence: 99%

“…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 ]. These results indicated that Enhancer-LSTMAtt is a competitive method to recognize enhancers.…”

Section: Resultsmentioning

confidence: 99%

“…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 ].…”

Section: Datamentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…In addition to SVM, other classifiers, such as K nearest neighbor (KNN), [10,16,51] decision tree (DT), [57] Naïve Bayes (NB), [51,57,58] and random forest (RF) [16,51,[57][58][59] are also often used in bioinformatics area. Hence, we also compared the classification performance of SVM and other classifiers.…”

Section: Comparison Between Svm and Other Classifiersmentioning

confidence: 99%

Identifying blood‐brain barrier peptides by using amino acids physicochemical properties and features fusion method

Zou

2021

Peptide Science

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Blood-brain barrier peptides (BBPs) play a promising role in current drug study of central nervous system diseases. Hence, it is an urgent need to rapidly and accurately discriminating BBPs from non-BBPs. Experimental approaches are the first choice, however, these methods are expensive and take a lot of time. Thus, more and more researchers focused their attention on computational models. In current work, we developed a support vector machine (SVM) based model to identify BBPs. First, amino acids physicochemical properties were employed to represent peptide sequences, and Pearson's correlation coefficient (PCC) and maximal information coefficient (MIC) were applied to extract useful information. Then, similarity network fusion algorithm was utilized to integrate these two different kinds of features. Next, Fisher algorithm was used to pick out the discriminative features. Finally, these selected features were input into SVM for distinguishing BBPs from non-BBPs. The proposed model achieved 100.00% and 89.47% classification accuracies on training and independent datasets, respectively. Additionally, we found that pK2 (NH3) property of amino acid plays a key role in discriminating BBPs from non-BBPs. The results showed that our proposed method is effective, and achieved a significantly improvement in identifying BBPs, as compared with the state-of-the-art approach. The Matlab codes and datasets are freely available at https://figshare.com/articles/ online_resource/iBBPs_zip/14723766.blood-brain barrier peptides, MIC, PCC, similarity network fusion algorithm, support vector machine | INTRODUCTIONCurrently, central nervous system (CNS) diseases, such as Alzheimer's disease, [1,2] Parkinson's disease, [1,2] and brain tumor [3] are a serious threat to people's health. With the increasing of incidence of CNS diseases, more and more resource was devoted to study and treat CNS diseases.It is reported that CNS diseases account for about 25% of the burden of all diseases in high-income countries and Europe. [4] At present, the blood-brain barrier (BBB) is one of the key points in treatment of CNS diseases, which prevents drugs to arrive at their targets. [3,5] Fortunately, recent study found that some peptides have the ability to cross the BBB, these peptides were called as blood-brain barrier peptides (BBPs). [6] BBPs play an important role in CNS study and often show hormesis, [7] which provide a new idea to diagnose and treat brain diseases. Compared with conventional drugs, BBPs with the following advantages that are low immunogenicity and toxicity. [7,8] Hence, BBPs may be an ideal choice for the treatment of CNS diseases.Up to now, many efforts have been made in investigating BBPs, however, it is still a challenging task to identify BBPs. Experimental approaches are the first choice in BBPs identification, but these methods are expensive and take a lot of time, so researchers turned their attention to computational approaches. In fact, various computational models

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A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome

Phan

He³

et al. 2023

Proteomics

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iEnhancer-RF: Identifying enhancers and their strength by enhanced feature representation using random forest

Cited by 27 publications

References 65 publications

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

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

Identifying blood‐brain barrier peptides by using amino acids physicochemical properties and features fusion method

A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome

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