{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T06:26:58Z","timestamp":1772519218583,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62163018"],"award-info":[{"award-number":["62163018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62163018"],"award-info":[{"award-number":["62163018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62163018"],"award-info":[{"award-number":["62163018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62163018"],"award-info":[{"award-number":["62163018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62163018"],"award-info":[{"award-number":["62163018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>This work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Extensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed methods.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04789-6","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T09:07:58Z","timestamp":1656493678000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction"],"prefix":"10.1186","volume":"23","author":[{"given":"Ying","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zequn","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niannian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"issue":"1","key":"4789_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/npp.2012.112","volume":"38","author":"LD Moore","year":"2013","unstructured":"Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38(1):23\u201338.","journal-title":"Neuropsychopharmacology"},{"key":"4789_CR2","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1590\/S0100-879X2005001000010","volume":"38","author":"K Santos","year":"2005","unstructured":"Santos K, Mazzola T, Carvalho H. The prima donna of epigenetics: the regulation of gene expression by DNA methylation. Braz J Med Biol Res. 2005;38:1531\u201341.","journal-title":"Braz J Med Biol Res"},{"issue":"22","key":"4789_CR3","doi-asserted-by":"publisher","first-page":"4632","DOI":"10.1200\/JCO.2004.07.151","volume":"22","author":"PM Das","year":"2004","unstructured":"Das PM, Singal R. DNA methylation and cancer. J Clin Oncol. 2004;22(22):4632\u201342.","journal-title":"J Clin Oncol"},{"issue":"1","key":"4789_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/0959-440X(95)80003-J","volume":"5","author":"X Cheng","year":"1995","unstructured":"Cheng X. DNA modification by methyltransferases. Curr Opin Struct Biol. 1995;5(1):4\u201310.","journal-title":"Curr Opin Struct Biol"},{"issue":"4501","key":"4789_CR5","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1126\/science.6262918","volume":"212","author":"M Ehrlich","year":"1981","unstructured":"Ehrlich M, Wang R. 5-methylcytosine in eukaryotic DNA. Science. 1981;212(4501):1350\u20137.","journal-title":"Science"},{"issue":"12","key":"4789_CR6","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1038\/nrm4076","volume":"16","author":"G-Z Luo","year":"2015","unstructured":"Luo G-Z, Blanco MA, Greer EL, He C, Shi Y. DNA n 6-methyladenine: a new epigenetic mark in eukaryotes? Nat Rev Mol Cell Biol. 2015;16(12):705\u201310.","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"8","key":"4789_CR7","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1074\/mcp.RA118.001169","volume":"18","author":"J Tang","year":"2019","unstructured":"Tang J, Fu J, Wang Y, Luo Y, Yang Q, Li B, Tu G, Hong J, Cui X, Chen Y, et al. Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains*[s]. Mol Cell Proteomics. 2019;18(8):1683\u201399.","journal-title":"Mol Cell Proteomics"},{"issue":"1","key":"4789_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.jid.2019.05.011","volume":"140","author":"F K\u00f6hler","year":"2020","unstructured":"K\u00f6hler F, Rodr\u00edguez-Paredes M. DNA methylation in epidermal differentiation, aging, and cancer. J Investig Dermatol. 2020;140(1):38\u201347.","journal-title":"J Investig Dermatol"},{"issue":"1","key":"4789_CR9","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1146\/annurev.ge.25.120191.001305","volume":"25","author":"P Modrich","year":"1991","unstructured":"Modrich P. Mechanisms and biological effects of mismatch repair. Annu Rev Genet. 1991;25(1):229\u201353.","journal-title":"Annu Rev Genet"},{"issue":"5","key":"4789_CR10","doi-asserted-by":"publisher","first-page":"633","DOI":"10.2144\/000112807","volume":"44","author":"HP Schweizer","year":"2008","unstructured":"Schweizer HP. Bacterial genetics: past achievements, present state of the field, and future challenges. Biotechniques. 2008;44(5):633\u201341.","journal-title":"Biotechniques"},{"key":"4789_CR11","doi-asserted-by":"crossref","unstructured":"Chung D, Farkas J, Huddleston JR, Olivar E, Westpheling J. Methylation by a unique $$\\alpha$$-class n4-cytosine methyltransferase is required for DNA transformation of caldicellulosiruptor bescii dsm6725. 2012.","DOI":"10.1371\/journal.pone.0043844"},{"issue":"4","key":"4789_CR12","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.molcel.2015.05.004","volume":"58","author":"JA Reuter","year":"2015","unstructured":"Reuter JA, Spacek DV, Snyder MP. High-throughput sequencing technologies. Mol Cell. 2015;58(4):586\u201397.","journal-title":"Mol Cell"},{"issue":"6","key":"4789_CR13","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1038\/nmeth.1459","volume":"7","author":"BA Flusberg","year":"2010","unstructured":"Flusberg BA, Webster DR, Lee JH, Travers KJ, Olivares EC, Clark TA, Korlach J, Turner SW. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods. 2010;7(6):461\u20135.","journal-title":"Nat Methods"},{"issue":"21","key":"4789_CR14","first-page":"148","volume":"43","author":"M Yu","year":"2015","unstructured":"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. Nucleic Acids Res. 2015;43(21):148\u2013148.","journal-title":"Nucleic Acids Res"},{"issue":"22","key":"4789_CR15","doi-asserted-by":"publisher","first-page":"3518","DOI":"10.1093\/bioinformatics\/btx479","volume":"33","author":"W Chen","year":"2017","unstructured":"Chen W, Yang H, Feng P, Ding H, Lin H. idna4mc: identifying DNA n4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics. 2017;33(22):3518\u201323.","journal-title":"Bioinformatics"},{"issue":"8","key":"4789_CR16","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1093\/bioinformatics\/bty824","volume":"35","author":"L Wei","year":"2019","unstructured":"Wei L, Luan S, Nagai LAE, Su R, Zou Q. Exploring sequence-based features for the improved prediction of DNA n4-methylcytosine sites in multiple species. Bioinformatics. 2019;35(8):1326\u201333.","journal-title":"Bioinformatics"},{"issue":"23","key":"4789_CR17","doi-asserted-by":"publisher","first-page":"4930","DOI":"10.1093\/bioinformatics\/btz408","volume":"35","author":"L Wei","year":"2019","unstructured":"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(23):4930\u20137.","journal-title":"Bioinformatics"},{"key":"4789_CR18","doi-asserted-by":"publisher","first-page":"105119","DOI":"10.1016\/j.compbiomed.2021.105119","volume":"140","author":"L Shen","year":"2022","unstructured":"Shen L, Liu F, Huang L, Liu G, Zhou L, Peng L. Vda-rwlrls: an anti-sars-cov-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares. Comput Biol Med. 2022;140:105119.","journal-title":"Comput Biol Med"},{"key":"4789_CR19","first-page":"1","volume":"14","author":"W Liu","year":"2021","unstructured":"Liu W, Jiang Y, Peng L, Sun X, Gan W, Zhao Q, Tang H. Inferring gene regulatory networks using the improved Markov blanket discovery algorithm. Interdiscip Sci Comput Life Sci. 2021;14:1\u201314.","journal-title":"Interdiscip Sci Comput Life Sci"},{"issue":"1","key":"4789_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-83737-5","volume":"11","author":"L Peng","year":"2021","unstructured":"Peng L, Shen L, Xu J, Tian X, Liu F, Wang J, Tian G, Yang J, Zhou L. Prioritizing antiviral drugs against sars-cov-2 by integrating viral complete genome sequences and drug chemical structures. Sci Rep. 2021;11(1):1\u201311.","journal-title":"Sci Rep"},{"key":"4789_CR21","doi-asserted-by":"publisher","first-page":"145455","DOI":"10.1109\/ACCESS.2019.2943169","volume":"7","author":"J Khanal","year":"2019","unstructured":"Khanal J, Nazari I, Tayara H, Chong KT. 4mccnn: identification of n4-methylcytosine sites in prokaryotes using convolutional neural network. IEEE Access. 2019;7:145455\u201361.","journal-title":"IEEE Access"},{"issue":"3","key":"4789_CR22","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1093\/bib\/bbaa124","volume":"22","author":"Q Liu","year":"2021","unstructured":"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. 2021;22(3):124.","journal-title":"Brief Bioinform"},{"issue":"11","key":"4789_CR23","doi-asserted-by":"publisher","first-page":"3327","DOI":"10.1093\/bioinformatics\/btaa143","volume":"36","author":"Q Tang","year":"2020","unstructured":"Tang Q, Kang J, Yuan J, Tang H, Li X, Lin H, Huang J, Chen W. DNA4mc-lip: a linear integration method to identify n4-methylcytosine site in multiple species. Bioinformatics. 2020;36(11):3327\u201335.","journal-title":"Bioinformatics"},{"issue":"4","key":"4789_CR24","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1093\/bioinformatics\/bty668","volume":"35","author":"W He","year":"2019","unstructured":"He W, Jia C, Zou Q. 4mcpred: machine learning methods for DNA n4-methylcytosine sites prediction. Bioinformatics. 2019;35(4):593\u2013601.","journal-title":"Bioinformatics"},{"key":"4789_CR25","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.omtn.2019.04.019","volume":"16","author":"B Manavalan","year":"2019","unstructured":"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 Ther Nucleic Acids. 2019;16:733\u201344.","journal-title":"Mol Ther Nucleic Acids"},{"issue":"3","key":"4789_CR26","doi-asserted-by":"publisher","first-page":"099","DOI":"10.1093\/bib\/bbaa099","volume":"22","author":"H Xu","year":"2021","unstructured":"Xu H, Jia P, Zhao Z. Deep4mc: systematic assessment and computational prediction for DNA n4-methylcytosine sites by deep learning. Brief Bioinform. 2021;22(3):099.","journal-title":"Brief Bioinform"},{"key":"4789_CR27","unstructured":"Ng P. dna2vec: consistent vector representations of variable-length k-mers. arXiv preprint arXiv:1701.06279 (2017)"},{"issue":"12","key":"4789_CR28","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1038\/nmeth.2646","volume":"10","author":"JP O\u2019shea","year":"2013","unstructured":"O\u2019shea JP, Chou MF, Quader SA, Ryan JK, Church GM, Schwartz D. plogo: a probabilistic approach to visualizing sequence motifs. Nat Methods. 2013;10(12):1211\u20132.","journal-title":"Nat Methods"},{"issue":"3","key":"4789_CR29","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M, et al. Classification and regression by randomforest. R News. 2002;2(3):18\u201322.","journal-title":"R News"},{"key":"4789_CR30","doi-asserted-by":"crossref","unstructured":"Schapire RE. Explaining adaboost. In: Empirical Inference, pp. 37\u201352. Springer; 2013.","DOI":"10.1007\/978-3-642-41136-6_5"},{"issue":"60","key":"4789_CR31","first-page":"1","volume":"18","author":"KP Murphy","year":"2006","unstructured":"Murphy KP, et al. Naive Bayes classifiers. University of British Columbia. 2006;18(60):1\u20138.","journal-title":"University of British Columbia"},{"issue":"1","key":"4789_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1139-1","volume":"18","author":"C Angermueller","year":"2017","unstructured":"Angermueller C, Lee HJ, Reik W, Stegle O. Deepcpg: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):1\u201313.","journal-title":"Genome Biol"},{"key":"4789_CR33","doi-asserted-by":"crossref","unstructured":"Zaitzeff A, Leiby N, Motta FC, Haase SB, Singer JM. Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins. bioRxiv 2021.","DOI":"10.1101\/2021.04.09.439184"},{"issue":"4","key":"4789_CR34","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/nar\/gkr1146","volume":"40","author":"TA Clark","year":"2012","unstructured":"Clark TA, Murray IA, Morgan RD, Kislyuk AO, Spittle KE, Boitano M, Fomenkov A, Roberts RJ, Korlach J. Characterization of DNA methyltransferase specificities using single-molecule, real-time DNA sequencing. Nucleic Acids Res. 2012;40(4):29\u201329.","journal-title":"Nucleic Acids Res"},{"key":"4789_CR35","doi-asserted-by":"crossref","unstructured":"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 2016;950.","DOI":"10.1093\/nar\/gkw950"},{"issue":"13","key":"4789_CR36","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","volume":"22","author":"W Li","year":"2006","unstructured":"Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658\u20139.","journal-title":"Bioinformatics"},{"key":"4789_CR37","doi-asserted-by":"crossref","unstructured":"Deng L, Wu H, Liu H. D2vcb: a hybrid deep neural network for the prediction of in-vivo protein-DNA binding from combined DNA sequence. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019;74\u201377. IEEE","DOI":"10.1109\/BIBM47256.2019.8983051"},{"issue":"1","key":"4789_CR38","first-page":"1","volume":"3","author":"A Yilmaz","year":"2020","unstructured":"Yilmaz A. Assessment of mutation susceptibility in DNA sequences with word vectors. J Intell Syst Theory Appl. 2020;3(1):1\u20136.","journal-title":"J Intell Syst Theory Appl"},{"key":"4789_CR39","doi-asserted-by":"crossref","unstructured":"Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In: International Conference on Artificial Neural Networks, 2011;44\u201351. Springer","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"4789_CR40","unstructured":"Sabour S, Frosst N, Hinton GE. Dynamic routing between capsules. arXiv preprint arXiv:1710.09829 2017."},{"issue":"23","key":"4789_CR41","first-page":"1","volume":"20","author":"BP Nguyen","year":"2019","unstructured":"Nguyen BP, Nguyen QH, Doan-Ngoc G-N, Nguyen-Vo T-H, Rahardja S. iprodna-capsnet: identifying protein-DNA binding residues using capsule neural networks. BMC Bioinform. 2019;20(23):1\u201312.","journal-title":"BMC Bioinform"},{"issue":"1","key":"4789_CR42","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1093\/bib\/bbab492","volume":"23","author":"J Khanal","year":"2022","unstructured":"Khanal J, Tayara H, Zou Q, To Chong K. Deepcap-kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network. Brief Bioinform. 2022;23(1):492.","journal-title":"Brief Bioinform"},{"key":"4789_CR43","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. In: Advances in Neural Information Processing Systems, 2017;5998\u20136008."},{"issue":"2","key":"4789_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-019-2713-1","volume":"63","author":"Q Wang","year":"2020","unstructured":"Wang Q, Huang Y, Jia W, He X, Blumenstein M, Lyu S, Lu Y. Faclstm: Convlstm with focused attention for scene text recognition. Sci China Inf Sci. 2020;63(2):1\u201314.","journal-title":"Sci China Inf Sci"},{"issue":"Supplement-2","key":"4789_CR45","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1093\/bioinformatics\/btaa891","volume":"36","author":"Y Long","year":"2020","unstructured":"Long Y, Wu M, Liu Y, Kwoh CK, Luo J, Li X. Ensembling graph attention networks for human microbe-drug association prediction. Bioinformatics. 2020;36(Supplement-2):779\u201386.","journal-title":"Bioinformatics"},{"key":"4789_CR46","doi-asserted-by":"crossref","unstructured":"Zhao Y, Jiang M, Kong J, Li S. Paralleled attention modules and adaptive focal loss for siamese visual tracking. IET Image Processing 2021.","DOI":"10.1049\/ipr2.12109"},{"issue":"4","key":"4789_CR47","first-page":"8","volume":"15","author":"B Nguyen-Xuan","year":"2019","unstructured":"Nguyen-Xuan B, Lee G-S. Sketch recognition using lstm with attention mechanism and minimum cost flow algorithm. Int J Contents. 2019;15(4):8\u201315.","journal-title":"Int J Contents"},{"issue":"24","key":"4789_CR48","doi-asserted-by":"publisher","first-page":"4223","DOI":"10.1093\/bioinformatics\/bty522","volume":"34","author":"F Li","year":"2018","unstructured":"Li F, Li C, Marquez-Lago TT, Leier A, Akutsu T, Purcell AW, Ian Smith A, Lithgow T, Daly RJ, Song J, et al. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics. 2018;34(24):4223\u201331.","journal-title":"Bioinformatics"},{"key":"4789_CR49","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.csbj.2020.04.001","volume":"18","author":"MM Hasan","year":"2020","unstructured":"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\u201312.","journal-title":"Comput Struct Biotechnol J"},{"issue":"4","key":"4789_CR50","doi-asserted-by":"publisher","first-page":"100991","DOI":"10.1016\/j.isci.2020.100991","volume":"23","author":"H Lv","year":"2020","unstructured":"Lv H, Dao F-Y, Zhang D, Guan Z-X, Yang H, Su W, Liu M-L, Ding H, Chen W, Lin H. idna-ms: an integrated computational tool for detecting DNA modification sites in multiple genomes. Iscience. 2020;23(4):100991.","journal-title":"Iscience"},{"key":"4789_CR51","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.ijbiomac.2019.12.009","volume":"157","author":"MM Hasan","year":"2020","unstructured":"Hasan MM, Manavalan B, Khatun MS, Kurata H. i4mc-rose, a bioinformatics tool for the identification of DNA n4-methylcytosine sites in the rosaceae genome. Int J Biol Macromol. 2020;157:752\u20138.","journal-title":"Int J Biol Macromol"},{"issue":"5","key":"4789_CR52","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1093\/bib\/bbz088","volume":"21","author":"B Rao","year":"2020","unstructured":"Rao B, Zhou C, Zhang G, Su R, Wei L. Acpred-fuse: fusing multi-view information improves the prediction of anticancer peptides. Brief Bioinform. 2020;21(5):1846\u201355.","journal-title":"Brief Bioinform"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04789-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04789-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04789-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:07:44Z","timestamp":1668478064000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04789-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4789"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04789-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]},"assertion":[{"value":"10 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"258"}}