An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

Nguyen, Duong; Tran, The-Anh; Le, Nguyen Quoc Khanh; Pham, Dinh-Minh; Ou, Yu‐Yen

doi:10.1109/tcbb.2021.3082184

Cited by 11 publications

(17 citation statements)

References 29 publications

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“…This may be caused by the extreme imbalance between the number of positive and negative samples in the dataset. In addition, 5mC_Pred provided an outstanding performance with the Sen value close to 0.9 and the highest MCC value, which adopted the FastText algorithm to generate embedding vectors [37]. This indicated that k-mers embeddings learned from a pre-trained language model could improve the capability of the model to discriminate 5mC sites from non-5mC sites.…”

Section: Methodsmentioning

confidence: 91%

“…Feature Algorithm iPromoter-5mC [36] One-hot Deep neural network 5mC_Pred [37] K-mers XGBoost BiLSTM-5mC (This study)…”

Section: Methodsmentioning

confidence: 99%

“…To the best of our knowledge, there are only two computational tools to identify 5mC sites in genome-wide DNA promoters on the same datasets, i.e., iPromoter-5mC [36] and 5mC_Pred [37]. Table 1 provides an overview of these tools and our model, including feature description methods and classification algorithms.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

“…Based on the one-hot encoding, iPromoter-5mC achieved the robust and reliable performance on the independent testing dataset. Subsequently, Nguyen et al compared the effectiveness of several of the most popular machine learning techniques including XGBoost, random forest, deep forest, and the deep feedforward neural network based on the same datasets [37]. Among them, the XGBoost classifier with the k-mers embeddings strategy showed the best achievement and outperformed the iPromoter-5mC model on both the 5-fold cross-validation (CV) and the independent test [37].…”

Section: Introductionmentioning

confidence: 99%

“…Molecules 2021, 26, x FOR PEER REVIEW 3 of 12 techniques including XGBoost, random forest, deep forest, and the deep feedforward neural network based on the same datasets [37]. Among them, the XGBoost classifier with the k-mers embeddings strategy showed the best achievement and outperformed the iPromoter-5mC model on both the 5-fold cross-validation (CV) and the independent test [37].…”

Section: Introductionmentioning

confidence: 99%

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BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters

Cheng

Wang

et al. 2021

Molecules

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An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC). Therefore, identifying the 5mC sites in the promoters is a critical step towards further understanding the diverse functions of DNA methylation in genetic diseases such as cancers and aging. However, most wet-lab experimental techniques are often time consuming and laborious for detecting 5mC sites. In this study, we proposed a deep learning-based approach, called BiLSTM-5mC, for accurately identifying 5mC sites in genome-wide DNA promoters. First, we randomly divided the negative samples into 11 subsets of equal size, one of which can form the balance subset by combining with the positive samples in the same amount. Then, two types of feature vectors encoded by the one-hot method, and the nucleotide property and frequency (NPF) methods were fed into a bidirectional long short-term memory (BiLSTM) network and a full connection layer to train the 22 submodels. Finally, the outputs of these models were integrated to predict 5mC sites by using the majority vote strategy. Our experimental results demonstrated that BiLSTM-5mC outperformed existing methods based on the same independent dataset.

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Section: Methodsmentioning

confidence: 91%

“…Feature Algorithm iPromoter-5mC [36] One-hot Deep neural network 5mC_Pred [37] K-mers XGBoost BiLSTM-5mC (This study)…”

Section: Methodsmentioning

confidence: 99%

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters

Cheng

Wang

et al. 2021

Molecules

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Enhanced 5mC-Methylation-Site Recognition in DNA Sequences using Token Classification and a Domain-specific Loss Function

Zeng

Huson

2023

Preprint

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The process of DNA 5-methylcytosine modification has been widely studied in mammals and and plays an important role in epigenetics. Several computational approaches have been developed to aid the identification of methylation sites. In this study, we introduce a novel deep-learning framework MR-DNR that aims at predicting specific methylation sites located in gene promoter regions. The idea is to adapt the name-entity recognition approach to methylation-site prediction. MR-DNA is trained on a stacked model architecture that consists of a pre-trained MuLan-Mehtyl-DistilBERT language model and conditional random field algorithms. The resulting fine-tuned model achieves an accuracy of 95.4% on an independent test dataset. A key advantage of this formulation of the methylation-site identification task is that the input DNA sequence can be of any length, unlike previous methods that predict methylation state on short, fixed-length DNA sequences. For training and testing purposes, we provide a database of DNA sequences containing verified 5mC methylation sites, obtained from eight human cell lines in ENCODE. Data and code are available at https://github.com/husonlab/MR-DNA.

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Aspirating Cell Into Orifice of Micropipette for Precise Cell Transportation Using Micropipette

Zhao

Sun

Zhao

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

Cited by 11 publications

References 29 publications

BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters

BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters

Enhanced 5mC-Methylation-Site Recognition in DNA Sequences using Token Classification and a Domain-specific Loss Function

Aspirating Cell Into Orifice of Micropipette for Precise Cell Transportation Using Micropipette

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