1
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Nguyen VN, Ho TT, Doan TD, Le NQK. Using a hybrid neural network architecture for DNA sequence representation: A study on N 4-methylcytosine sites. Comput Biol Med 2024; 178:108664. [PMID: 38875905 DOI: 10.1016/j.compbiomed.2024.108664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/11/2024] [Accepted: 05/26/2024] [Indexed: 06/16/2024]
Abstract
N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities.
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Affiliation(s)
- Van-Nui Nguyen
- University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Viet Nam
| | - Trang-Thi Ho
- Department of Computer Science and Information Engineering, TamKang University, New Taipei, 251301, Taiwan
| | - Thu-Dung Doan
- International Degree Program in Animal Vaccine Technology, International College, National Pingtung University of Science and Technology, Pingtung, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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2
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Hou A, Luo H, Liu H, Luo L, Ding P. Multi-scale DNA language model improves 6 mA binding sites prediction. Comput Biol Chem 2024; 112:108129. [PMID: 39067351 DOI: 10.1016/j.compbiolchem.2024.108129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 07/30/2024]
Abstract
DNA methylation at the N6 position of adenine (N6-methyladenine, 6 mA), which refers to the attachment of a methyl group to the N6 site of the adenine (A) of DNA, is an important epigenetic modification in prokaryotic and eukaryotic genomes. Accurately predicting the 6 mA binding sites can provide crucial insights into gene regulation, DNA repair, disease development and so on. Wet experiments are commonly used for analyzing 6 mA binding sites. However, they suffer from high cost and expensive time. Therefore, various deep learning methods have been widely used to predict 6 mA binding sites recently. In this study, we develop a framework based on multi-scale DNA language model named "iDNA6mA-MDL". "iDNA6mA-MDL" integrates multiple kmers and the nucleotide property and frequency method for feature embedding, which can capture a full range of DNA sequence context information. At the prediction stage, it also leverages DNABERT to compensate for the incomplete capture of global DNA information. Experiments show that our framework obtains average AUC of 0.981 on a classic 6 mA rice gene dataset, going beyond all existing advanced models under fivefold cross-validations. Moreover, "iDNA6mA-MDL" outperforms most of the popular state-of-the-art methods on another 11 6 mA datasets, demonstrating its effectiveness in 6 mA binding sites prediction.
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Affiliation(s)
- Anlin Hou
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Hanyu Luo
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Huan Liu
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang 421001, China.
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
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3
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Yao Z, Li F, Xie W, Chen J, Wu J, Zhan Y, Wu X, Wang Z, Zhang G. DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features. Comput Biol Med 2024; 171:108166. [PMID: 38382385 DOI: 10.1016/j.compbiomed.2024.108166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/23/2024]
Abstract
N4-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Traditional laboratory methods for detecting 4mC DNA methylation have limitations, but the rise of artificial intelligence has introduced efficient computational strategies for 4mC site prediction. Despite this progress, challenges persist in terms of model performance and interpretability. To tackle these challenges, we propose DeepSF-4mC, a deep learning model specifically designed for predicting DNA cytosine 4mC methylation sites by leveraging sequence features. Our approach incorporates multiple encoding techniques to enhance prediction accuracy, increase model stability, and reduce the computational resources needed. Leveraging transfer learning, we harness existing models to enhance performance through learned representations or fine-tuning. Ensemble learning techniques combine predictions from multiple models, boosting robustness and accuracy. This research contributes to DNA methylation analysis and lays the groundwork for understanding 4mC's multifaceted role in biological processes. The web server for DeepSF-4mC is accessible at: http://deepsf-4mc.top/and the original code can be found at: https://github.com/754131799/DeepSF-4mC.
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Affiliation(s)
- Zhaomin Yao
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Fei Li
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Weiming Xie
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Jiaming Chen
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Jiezhang Wu
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Ying Zhan
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Xiaodan Wu
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
| | - Guoxu Zhang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
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4
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Sultana A, Mitu SJ, Pathan MN, Uddin MN, Uddin MA, Aryal S. 4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome. Comput Biol Chem 2023; 107:107974. [PMID: 37944386 DOI: 10.1016/j.compbiolchem.2023.107974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/22/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs. In our research work, we propose a 4mC-CGRU which is a deep learning-based computational model with a standard encoding method to identify the 4mC sites from DNA sequences that learned autonomous feature selection in the Rosaceae genome, particularly in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca). The proposed model consists of a convolutional neural network (CNN) and a gated recurrent unit network (GRU)-based model for identifying 4mC sites from Fragaria vesca and Rosa chinensis in the genomes. The CNN model extracts useful features from the datasets and the GRU classifies the DNA sequences. Thus, our approach can automatically extract important features to detect relative sites from DNA sequence. The performance analysis shows that the proposed model consistently outperforms over the state-of-the-art works in detecting 4mC sites.
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Affiliation(s)
- Abida Sultana
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh.
| | - Sadia Jannat Mitu
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Md Naimul Pathan
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh.
| | - Mohammed Nasir Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Md Ashraf Uddin
- School of Information Technology, Deakin University Geelong, Australia.
| | - Sunil Aryal
- School of Information Technology, Deakin University Geelong, Australia.
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Ju H, Bai J, Jiang J, Che Y, Chen X. Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning. Front Genet 2023; 14:1254827. [PMID: 37671040 PMCID: PMC10476523 DOI: 10.3389/fgene.2023.1254827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, Harbin, China
| | - Jie Bai
- Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Hangzhou, China
| | - Jing Jiang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yusheng Che
- Heilongjiang Agricultural Engineering Vocational College, Harbin, China
| | - Xin Chen
- Department of Neurosurgical Laboratory, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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Yu X, Ren J, Cui Y, Zeng R, Long H, Ma C. DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era. Front Med (Lausanne) 2023; 10:1187430. [PMID: 37215722 PMCID: PMC10192687 DOI: 10.3389/fmed.2023.1187430] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction The DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites. Methods The model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW. Results and Discussion The results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era.
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Affiliation(s)
- Xia Yu
- School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Jia Ren
- Industrial Design School, Shandong University of ART and Design, Jinan, Shandong, China
| | - Yani Cui
- School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China
| | - Rao Zeng
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Haixia Long
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Cuihua Ma
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
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Liu Z, Lan P, Liu T, Liu X, Liu T. m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier. Int J Mol Sci 2023; 24:ijms24097878. [PMID: 37175594 PMCID: PMC10177809 DOI: 10.3390/ijms24097878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron-ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am.
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Affiliation(s)
- Ze Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
| | - Pengfei Lan
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
| | - Ting Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong 999077, China
| | - Xudong Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Tao Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
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8
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Qu Y, Wen Y, Chen M, Guo K, Huang X, Gu L. Predicting case difficulty in endodontic microsurgery using machine learning algorithms. J Dent 2023; 133:104522. [PMID: 37080531 DOI: 10.1016/j.jdent.2023.104522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 04/22/2023] Open
Abstract
OBJECTIVES The study aimed to develop and validate machine learning models for case difficulty prediction in endodontic microsurgery, assisting clinicians in preoperative analysis. METHODS The cone-beam computed tomographic images were collected from 261 patients with 341 teeth and used for radiographic examination and measurement. Through linear regression (LR), support vector regression (SVR), and extreme gradient boosting (XGBoost) algorithms, four models were established according to different loss functions, including the L1-loss LR model, L2-loss LR model, SVR model and XGBoost model. Five-fold cross-validation was applied in model training and validation. Explained variance score (EVS), coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE) and median absolute error (MedAE) were calculated to evaluate the prediction performance. RESULTS The MAE, MSE and MedAE values of the XGBoost model were the lowest, which were 0.1010, 0.0391 and 0.0235, respectively. The EVS and R2 values of the XGBoost model were the highest, which were 0.7885 and 0.7967, respectively. The factors used to predict the case difficulty in endodontic microsurgery were ordered according to their relative importance, including lesion size, the distance between apex and adjacent important anatomical structures, root filling density, root apex diameter, root resorption, tooth type, tooth length, root filling length, root canal curvature and the number of root canals. CONCLUSIONS The XGBoost model outperformed the LR and SVR models on all evaluation metrics, which can assist clinicians in preoperative analysis. The relative feature importance provides a reference to develop the scoring system for case difficulty assessment in endodontic microsurgery. CLINICAL SIGNIFICANCE Preoperative case assessment is a crucial step to identify potential risks and make referral decisions. Machine learning models for case difficulty prediction in endodontic microsurgery can assist clinicians in preoperative analysis efficiently and accurately.
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Affiliation(s)
- Yang Qu
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Yiting Wen
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Ming Chen
- South China University of Technology, Guangzhou, China
| | - Kailing Guo
- South China University of Technology, Guangzhou, China
| | - Xiangya Huang
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
| | - Lisha Gu
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
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9
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Yang S, Yang Z, Yang J. 4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies. Int J Biol Macromol 2023; 231:123180. [PMID: 36646347 DOI: 10.1016/j.ijbiomac.2023.123180] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/26/2022] [Accepted: 12/30/2022] [Indexed: 01/15/2023]
Abstract
N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.
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Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China; The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou 213164, China.
| | - Zexi Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China
| | - Jun Yang
- School of Educational Sciences, Yili Normal University, Yining 835000, China
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10
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A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach. Genes (Basel) 2023; 14:genes14030582. [PMID: 36980853 PMCID: PMC10048346 DOI: 10.3390/genes14030582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 03/02/2023] Open
Abstract
DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction–modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method “word2vec” and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model’s efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.
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11
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Nabeel Asim M, Ali Ibrahim M, Fazeel A, Dengel A, Ahmed S. DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method. Brief Bioinform 2023; 24:6931721. [PMID: 36528802 DOI: 10.1093/bib/bbac546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/06/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022] Open
Abstract
Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Ahtisham Fazeel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
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12
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Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit. Interdiscip Sci 2022; 14:879-894. [PMID: 35474167 DOI: 10.1007/s12539-022-00521-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 12/30/2022]
Abstract
Hypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. Therefore, we develop a new model to identify AHTPs based on multiple features and deep learning. And the deep model is constructed by combining a convolutional neural network (CNN) and a gated recurrent unit (GRU). The unique convolution structure is used to reduce the feature dimension and running time. The data processed by CNN is input into the recurrent structure GRU, and important information is filtered out through the reset gate and update gate. Finally, the output layer adopts Sigmoid activation function. Firstly, we use Kmer, the deviation between the dipeptide frequency and the expected mean (DDE), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) and dipeptide binary profile and frequency (DBPF) to extract features. For Kmer, DDE, EBGW and EGAAC, it is widely used in the field of protein research. DBPF is a new feature representation method designed by us. It corresponds dipeptides to binary numbers, and finally obtains a binary coding file and a frequency file. Then these features are spliced together and input into our proposed model for prediction and analysis. After a tenfold cross-validation test, this model has a better competitive advantage than the previous methods, and the accuracy is 96.23% and 99.10%, respectively. From the results, compared with the previous methods, it has been greatly improved. It shows that the combination of convolution calculation and recurrent structure has a positive impact on the classification of AHTPs. The results show that this method is a feasible, efficient and competitive sequence analysis tool for AHTPs. Meanwhile, we design a friendly online prediction tool and it is freely accessible at http://ahtps.zhanglab.site/ .
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Wang H, Li H, Gao W, Xie J. PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy. Anal Biochem 2022; 658:114935. [PMID: 36206844 DOI: 10.1016/j.ab.2022.114935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 12/30/2022]
Abstract
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.
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Affiliation(s)
- Houqiang Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Hong Li
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Weifeng Gao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Jin Xie
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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14
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Jin J, Yu Y, Wang R, Zeng X, Pang C, Jiang Y, Li Z, Dai Y, Su R, Zou Q, Nakai K, Wei L. iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations. Genome Biol 2022; 23:219. [PMID: 36253864 PMCID: PMC9575223 DOI: 10.1186/s13059-022-02780-1] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/03/2022] [Indexed: 11/29/2022] Open
Abstract
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.
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Affiliation(s)
- Junru Jin
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yingying Yu
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Xin Zeng
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.,Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan
| | - Chao Pang
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yi Jiang
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan, 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yutong Dai
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.,Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Kenta Nakai
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan. .,Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China. .,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
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15
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PSP-PJMI: An innovative feature representation algorithm for identifying DNA N4-methylcytosine sites. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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16
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Zhanga S, Yao Y, Wang J, Liang Y. Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree. Anal Biochem 2022; 652:114746. [DOI: 10.1016/j.ab.2022.114746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022]
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17
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Yu L, Zhang Y, Xue L, Liu F, Chen Q, Luo J, Jing R. Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning. Front Microbiol 2022; 13:843425. [PMID: 35401453 PMCID: PMC8989013 DOI: 10.3389/fmicb.2022.843425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
DNA N4-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Yonglin Zhang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Qi Chen
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China.,Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
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18
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Mouse4mC-BGRU: deep learning for predicting DNA N4-methylcytosine sites in mouse genome. Methods 2022; 204:258-262. [DOI: 10.1016/j.ymeth.2022.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
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