Greenberg MVC, Bourc’his D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol. 2019;20:590–607. https://doi.org/10.1038/s41580-019-0159-6.
Article
CAS
Google Scholar
Unnikrishnan A, Freeman WM, Jackson J, Wren JD, Porter H, Richardson A. The role of DNA methylation in epigenetics of aging. Pharmacol Ther. 2019;195:172–85. https://doi.org/10.1016/j.pharmthera.2018.11.001.
Article
CAS
Google Scholar
Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, Smits KM, Veeck J, Herman JG, Van Neste L, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;15:459–66. https://doi.org/10.1038/s41571-018-0004-4.
Article
CAS
Google Scholar
Baylin S. DNA methylation and gene silencing in cancer. Nat Clin Pract Oncol. 2005;2:S4–11. https://doi.org/10.1038/ncponc0354.
Article
CAS
Google Scholar
Zhao LY, Song J, Liu Y, Song CX, Yi C. Mapping the epigenetic modifications of DNA and RNA. Protein Cell. 2020;11:792–808. https://doi.org/10.1007/s13238-020-00733-7.
Article
CAS
Google Scholar
Ramsawhook AH, Lewis LC, Eleftheriou M, Abakir A, Durczak P, Markus R, Rajani S, Hannan NRF, Coyle B, Ruzov A. Immunostaining for DNA modifications: computational analysis of confocal images. J Vis Exp. 2017. https://doi.org/10.3791/56318.
Article
Google Scholar
Yang S, Wang Y, Chen Y, Dai Q. MASQC: next generation sequencing assists third generation sequencing for quality control in N6-methyladenine DNA identification. Front Genet. 2020;11:269. https://doi.org/10.3389/fgene.2020.00269.
Article
CAS
Google Scholar
Costello JF, Plass C. Methylation matters. J Med Genet. 2001;38:285–303. https://doi.org/10.1136/jmg.38.5.285.
Article
CAS
Google Scholar
Chen W, Yang H, Feng P, Ding H, Lin H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics. 2017;33:3518–23. https://doi.org/10.1093/bioinformatics/btx479.
Article
CAS
Google Scholar
Ehrlich M, Wang RY. 5-Methylcytosine in eukaryotic DNA. Science. 1981;212:1350–7. https://doi.org/10.1126/science.6262918.
Article
CAS
Google Scholar
Davis BM, Chao MC, Waldor MK. Entering the era of bacterial epigenomics with single molecule real time DNA sequencing. Curr Opin Microbiol. 2013;16:192–8. https://doi.org/10.1016/j.mib.2013.01.011.
Article
CAS
Google Scholar
Pataillot-Meakin T, Pillay N, Beck S. 3-methylcytosine in cancer: an underappreciated methyl lesion? Epigenomics. 2016;8:451–4. https://doi.org/10.2217/epi.15.121.
Article
CAS
Google Scholar
Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38:23–38. https://doi.org/10.1038/npp.2012.112.
Article
CAS
Google Scholar
Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13:484–92. https://doi.org/10.1038/nrg3230.
Article
CAS
Google Scholar
Ling C, Groop L. Epigenetics: a molecular link between environmental factors and type 2 diabetes. Diabetes. 2009;58:2718–25. https://doi.org/10.2337/db09-1003.
Article
CAS
Google Scholar
Yao B, Jin P. Cytosine modifications in neurodevelopment and diseases. Cell Mol Life Sci. 2014;71:405–18. https://doi.org/10.1007/s00018-013-1433-y.
Article
CAS
Google Scholar
Hou R, Wu J, Xu L, Zou Q, Wu Y-J. Computational prediction of protein arginine methylation based on composition–transition–distribution features. ACS Omega. 2020;5:27470–9. https://doi.org/10.1021/acsomega.0c03972.
Article
CAS
Google Scholar
Manavalan B, Hasan MM, Basith S, Gosu V, Shin T-H, Lee G. Empirical comparison and analysis of web-based DNA N4-methylcytosine site prediction tools. Mol Ther Nucl Acids. 2020;22:406–20. https://doi.org/10.1016/j.omtn.2020.09.010.
Article
CAS
Google Scholar
Khanal J, Tayara H, Zou Q, Chong KT. Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation. Comput Struct Biotechnol J. 2021;19:1612–9. https://doi.org/10.1016/j.csbj.2021.03.015.
Article
CAS
Google Scholar
Yu M, Ji L, Neumann DA, Chung D-H, Groom J, Westpheling J, He C, Schmitz RJ. Base-resolution detection of N 4-methylcytosine in genomic DNA using 4mC-Tet-assisted-bisulfite-sequencing. Nucl Acids Res. 2015. https://doi.org/10.1093/nar/gkv738.
Article
Google Scholar
Huang G, Shen Q, Zhang G, Wang P, Yu ZG. LSTMCNNsucc: a bidirectional LSTM and CNN-based deep learning method for predicting lysine succinylation sites. Biomed Res Int. 2021;2021:9923112. https://doi.org/10.1155/2021/9923112.
Article
CAS
Google Scholar
Huang G, Zheng Y, Wu YQ, Han GS, Yu ZG. An information entropy-based approach for computationally identifying histone lysine butyrylation. Front Genet. 2019;10:1325. https://doi.org/10.3389/fgene.2019.01325.
Article
CAS
Google Scholar
Huang G, Zeng W. A discrete hidden Markov model for detecting histone crotonyllysine sites. MATCH Commun Math Comput Chem. 2016;75:717–30.
Google Scholar
Lv Z, Zhang J, Ding H, Zou Q. RF-PseU: a random forest predictor for RNA pseudouridine sites. Front Bioeng Biotechnol. 2020;8:134. https://doi.org/10.3389/fbioe.2020.00134.
Article
Google Scholar
Chen W, Xing P, Zou Q. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines. Sci Rep. 2017;7:1–8. https://doi.org/10.1038/srep40242.
Article
CAS
Google Scholar
He W, Jia C, Zou Q. 4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction. Bioinformatics. 2019;35:593–601. https://doi.org/10.1093/bioinformatics/bty668.
Article
CAS
Google Scholar
Dai Q, Bao C, Hai Y, Ma S, Zhou T, Wang C, Wang Y, Huo W, Liu X, Yao Y, et al. MTGIpick allows robust identification of genomic islands from a single genome. Brief Bioinform. 2018;19:361–73. https://doi.org/10.1093/bib/bbw118.
Article
CAS
Google Scholar
Kulmanov M, Hoehndorf R. DeepGOPlus: improved protein function prediction from sequence. Bioinformatics. 2020;36:422–9. https://doi.org/10.1093/bioinformatics/btz595.
Article
CAS
Google Scholar
Yu G, Zhao Y, Lu C, Wang J. HashGO: hashing gene ontology for protein function prediction. Comput Biol Chem. 2017;71:264–73. https://doi.org/10.1016/j.compbiolchem.2017.09.010.
Article
CAS
Google Scholar
Callaway E. “It will change everything”: DeepMind’s AI makes gigantic leap in solving protein structures. Nature. 2020;588:203–5. https://doi.org/10.1038/d41586-020-03348-4.
Article
CAS
Google Scholar
Saberi-Movahed F, Rostami M, Berahmand K, Karami S, Tiwari P, Oussalah M, Band SS. Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection. Knowl Based Syst. 2022;256:109884. https://doi.org/10.1016/j.knosys.2022.109884.
Article
Google Scholar
Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M. Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput Biol Med. 2022;147:105766. https://doi.org/10.1016/j.compbiomed.2022.105766.
Article
CAS
Google Scholar
Rostami M, Oussalah M, Farrahi V. A novel time-aware food recommender-system based on deep learning and graph clustering. IEEE Access. 2022;10:52508–24.
Article
Google Scholar
Manavalan B, Basith S, Shin TH, Wei L, Lee G. Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Mol Therapy-Nucl Acids. 2019;16:733–44. https://doi.org/10.1016/j.omtn.2019.04.019.
Article
CAS
Google Scholar
Wei L, Su R, Luan S, Liao Z, Manavalan B, Zou Q, Shi X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics. 2019;35:4930–7. https://doi.org/10.1093/bioinformatics/btz408.
Article
CAS
Google Scholar
Manavalan B, Basith S, Shin TH, Lee DY, Wei L, Lee G. 4mCpred-EL: an ensemble learning framework for identification of DNA N(4)-methylcytosine sites in the mouse genome. Cells. 2019;8:1332. https://doi.org/10.3390/cells8111332.
Article
CAS
Google Scholar
Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H. i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Comput Struct Biotechnol J. 2020;18:906–12. https://doi.org/10.1016/j.csbj.2020.04.001.
Article
CAS
Google Scholar
Abbas Z, Tayara H, Chong KT. 4mCPred-CNN—prediction of DNA N4-methylcytosine in the mouse genome using a convolutional neural network. Genes. 2021;12:296. https://doi.org/10.3390/genes12020296.
Article
CAS
Google Scholar
Jin J, Yu Y, Wei L. Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome. Methods. 2022;204:258–62. https://doi.org/10.1016/j.ymeth.2022.01.009.
Article
CAS
Google Scholar
Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song X-M, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MBE. 2021;18:3348–63. https://doi.org/10.3934/mbe.2021167.
Article
Google Scholar
Li Y, Zhao Z, Teng Z, Scribante A. i4mC-EL: identifying DNA N4-methylcytosine sites in the mouse genome using ensemble learning. Biomed Res Int. 2021;2021:1–11. https://doi.org/10.1155/2021/5515342.
Article
CAS
Google Scholar
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;26:3111–9.
Google Scholar
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781. 2013. https://doi.org/10.48550/arXiv.1301.3781.
Liu Q, Chen J, Wang Y, Li S, Jia C, Song J, Li F. DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites. Brief Bioinform. 2020. https://doi.org/10.1093/bib/bbaa124.
Article
Google Scholar
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA; 2017. p. 6000-10
Google Scholar
Devlin J, Chang M-W, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805. 2018. https://doi.org/10.48550/arXiv.1810.04805.
Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Process. 1997;45:2673–81. https://doi.org/10.1109/78.650093.
Article
Google Scholar
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77. https://doi.org/10.1016/j.patcog.2017.10.013.
Article
Google Scholar
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35:1285–98. https://doi.org/10.1109/TMI.2016.2528162.
Article
Google Scholar
Inglesfield J. A method of embedding. J Phys C: Solid State Phys. 1981;14:3795.
Article
CAS
Google Scholar
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1:541–51. https://doi.org/10.1162/neco.1989.1.4.541.
Article
Google Scholar
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–324. https://doi.org/10.1109/5.726791.
Article
Google Scholar
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition: 2009. IEEE: 248–255.
Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors. European conference on computer vision. Springer; 2014. p. 818–33.
Google Scholar
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014. https://doi.org/10.48550/arXiv.1409.1556.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2015. 1–9.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2016. 770–778.
Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET): 2017. IEEE: 1–6.
Yin X, Goudriaan J, Lantinga EA, Vos J, Spiertz HJ. A flexible sigmoid function of determinate growth. Ann Bot. 2003;91:361–71. https://doi.org/10.1093/aob/mcg029.
Article
Google Scholar
Fan E. Extended tanh-function method and its applications to nonlinear equations. Phys Lett A. 2000;277:212–8. https://doi.org/10.1016/S0375-9601(00)00725-8.
Article
CAS
Google Scholar
Agarap AF. Deep learning using rectified linear units (relu). arXiv preprint arXiv:180308375. 2018. https://doi.org/10.48550/arXiv.1803.08375.
Olah C. Understanding lstm networks. 2015.
Bengio Y. Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML workshop on unsupervised and transfer learning: 2012. JMLR Workshop and Conference Proceedings: 17–36.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.
Article
CAS
Google Scholar
Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:150600019. 2015. https://doi.org/10.48550/arXiv.1506.00019.
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:12070580. 2012. https://doi.org/10.48550/arXiv.1207.0580.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;60:84–90. https://doi.org/10.1145/3065386.
Article
Google Scholar
Bouthillier X, Konda K, Vincent P, Memisevic R. Dropout as data augmentation. arXiv preprint arXiv:150608700. 2015. https://doi.org/10.48550/arXiv.1506.08700.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929–58.
Google Scholar
Ye P, Luan Y, Chen K, Liu Y, Xiao C, Xie Z. MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing. Nucleic Acids Res. 2017;45:D85–9. https://doi.org/10.1093/nar/gkw950.
Article
CAS
Google Scholar
Clough E, Barrett T. The gene expression omnibus database. In: Mathé E, Davis S, editors. Statistical genomics. Springer; 2016. p. 93–110.
Chapter
Google Scholar
Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2010;39:D19–21. https://doi.org/10.1093/nar/gkq1019.
Article
CAS
Google Scholar
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9. https://doi.org/10.1093/bioinformatics/btl158.
Article
CAS
Google Scholar