{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:00:04Z","timestamp":1762783204324,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"University Natural Science Research Project of Anhui Province","award":["2023AH050998"],"award-info":[{"award-number":["2023AH050998"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"DOI":"10.1145\/3698587.3701378","type":"proceedings-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:05:08Z","timestamp":1734343508000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["iDNA-EBT: An ensemble model based on multi-scale secondary fine-tuned BERT"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4528-5722","authenticated-orcid":false,"given":"Wei","family":"Peng","sequence":"first","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8926-5330","authenticated-orcid":false,"given":"Yueran","family":"Hu","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1174-3561","authenticated-orcid":false,"given":"Zihan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6346-0560","authenticated-orcid":false,"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3907-1318","authenticated-orcid":false,"given":"Hongwei","family":"Xia","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1967-2806","authenticated-orcid":false,"given":"Xiaolei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg1655"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2017.0078"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Y. Bergman H. Cedar DNA methylation dynamics in health and disease Nature structural & molecular biology 20(3) (2013) 274--281.","DOI":"10.1038\/nsmb.2518"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"S. Maegawa G. Hinkal H.S. Kim L. Shen L. Zhang J. Zhang N. Zhang S. Liang L.A. Donehower J.-P.J. Issa Widespread and tissue specific age-related DNA methylation changes in mice Genome research 20(3) (2010) 332--340.","DOI":"10.1101\/gr.096826.109"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"J. Casades\u00fas D. Low Epigenetic gene regulation in the bacterial world Microbiology and molecular biology reviews 70(3) (2006) 830--856.","DOI":"10.1128\/MMBR.00016-06"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"M. Yassi A. Chatterjee M. Parry Application of deep learning in cancer epigenetics through DNA methylation analysis Briefings in bioinformatics 24(6) (2023) bbad411.","DOI":"10.1093\/bib\/bbad411"},{"key":"e_1_3_2_2_7_1","volume-title":"Galactica: A large language model for science, arXiv preprint arXiv:2211.09085","author":"Taylor M.","year":"2022","unstructured":"R. Taylor, M. Kardas, G. Cucurull, T. Scialom, A. Hartshorn, E. Saravia, A. Poulton, V. Kerkez, R. Stojnic, Galactica: A large language model for science, arXiv preprint arXiv:2211.09085 (2022)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"A.J. Thirunavukarasu D.S.J. Ting K. Elangovan L. Gutierrez T.F. Tan D.S.W. Ting Large language models in medicine Nature medicine 29(8) (2023) 1930--1940.","DOI":"10.1038\/s41591-023-02448-8"},{"key":"e_1_3_2_2_9_1","volume-title":"Bloomberggpt: A large language model for finance, arXiv preprint arXiv:2303.17564","author":"Wu O.","year":"2023","unstructured":"S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, G. Mann, Bloomberggpt: A large language model for finance, arXiv preprint arXiv:2303.17564 (2023)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"S.A. Smallwood H.J. Lee C. Angermueller F. Krueger H. Saadeh J. Peat S.R. Andrews O. Stegle W. Reik G. Kelsey Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity Nature methods 11(8) (2014) 817--820.","DOI":"10.1038\/nmeth.3035"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"M. Farlik N.C. Sheffield A. Nuzzo P. Datlinger A. Sch\u00f6negger J. Klughammer C. Bock Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics Cell reports 10(8) (2015) 1386--1397.","DOI":"10.1016\/j.celrep.2015.02.001"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"H. Guo P. Zhu X. Wu X. Li L. Wen F. Tang Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing Genome research 23(12) (2013) 2126--2135.","DOI":"10.1101\/gr.161679.113"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Y. Hou H. Guo C. Cao X. Li B. Hu P. Zhu X. Wu L. Wen F. Tang Y. Huang Single-cell triple omics sequencing reveals genetic epigenetic and transcriptomic heterogeneity in hepatocellular carcinomas Cell research 26(3) (2016) 304--319.","DOI":"10.1038\/cr.2016.23"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"B.A. Flusberg D.R. Webster J.H. Lee K.J. Travers E.C. Olivares T.A. Clark J. Korlach S.W. Turner Direct detection of DNA methylation during single-molecule real-time sequencing Nature methods 7(6) (2010) 461--465.","DOI":"10.1038\/nmeth.1459"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"G. Landan N.M. Cohen Z. Mukamel A. Bar A. Molchadsky R. Brosh S. Horn-Saban D.A. Zalcenstein N. Goldfinger A. Zundelevich Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues Nature genetics 44(11) (2012) 1207--1214.","DOI":"10.1038\/ng.2442"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3117"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty824"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"L. Yu Y. Zhang L. Xue F. Liu Q. Chen J. Luo R. Jing Systematic analysis and accurate identification of DNA N4-methylcytosine sites by deep learning Frontiers in microbiology 13 (2022) 843425.","DOI":"10.3389\/fmicb.2022.843425"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac671"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Q. Liu J. Chen Y. Wang S. Li C. Jia J. Song F. Li DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites Briefings in bioinformatics 22(3) (2021) bbaa124.","DOI":"10.1093\/bib\/bbaa124"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"C. Zhou C. Wang H. Liu Q. Zhou Q. Liu Y. Guo T. Peng J. Song J. Zhang L. Chen Identification and analysis of adenine N 6-methylation sites in the rice genome Nature plants 4(8) (2018) 554--563.","DOI":"10.1038\/s41477-018-0214-x"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes10100828"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2019.06.024"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz556"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Z. Li H. Jiang L. Kong Y. Chen K. Lang X. Fan L. Zhang C. Pian Deep6mA: a deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species PLoS computational biology 17(2) (2021) e1008767.","DOI":"10.1371\/journal.pcbi.1008767"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"X. Yang X. Ye X. Li L. Wei iDNA-MT: identification DNA modification sites in multiple species by using multi-task learning based a neural network tool Frontiers in genetics 12 (2021) 663572.","DOI":"10.3389\/fgene.2021.663572"},{"key":"e_1_3_2_2_27_1","volume-title":"Iscience 23(4)","author":"Lv F.-Y.","year":"2020","unstructured":"H. Lv, F.-Y. Dao, D. Zhang, Z.-X. Guan, H. Yang, W. Su, M.-L. Liu, H. Ding, W. Chen, H. Lin, iDNA-MS: an integrated computational tool for detecting DNA modification sites in multiple genomes, Iscience 23(4) (2020)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab677"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"J. Jin Y. Yu R. Wang X. Zeng C. Pang Y. Jiang Z. Li Y. Dai R. Su Q. Zou iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations Genome biology 23(1) (2022) 219.","DOI":"10.1186\/s13059-022-02780-1"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab083"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.16600"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.10.005"}],"event":{"name":"BCB '24: 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"],"location":"Shenzhen China","acronym":"BCB '24"},"container-title":["Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698587.3701378","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698587.3701378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:24:53Z","timestamp":1755861893000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698587.3701378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":32,"alternative-id":["10.1145\/3698587.3701378","10.1145\/3698587"],"URL":"https:\/\/doi.org\/10.1145\/3698587.3701378","relation":{},"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"2024-12-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}