{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:21:18Z","timestamp":1743006078626,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984343"},{"type":"electronic","value":"9789819984350"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8435-0_15","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"188-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DeepChrom: A Diffusion-Based Framework for\u00a0Long-Tailed Chromatin State Prediction"],"prefix":"10.1007","author":[{"given":"Yuhang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaheng","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"issue":"8","key":"15_CR1","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1038\/nbt.1662","volume":"28","author":"J Ernst","year":"2010","unstructured":"Ernst, J., Kellis, M.: Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28(8), 817\u2013825 (2010)","journal-title":"Nat. Biotechnol."},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Orouji, E., Raman, A.T.: Computational methods to explore chromatin state dynamics. Briefings in Bioinformatics 23(6), bbac439 (2022)","DOI":"10.1093\/bib\/bbac439"},{"issue":"10","key":"15_CR3","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1038\/s41576-022-00493-6","volume":"23","author":"S Dupont","year":"2022","unstructured":"Dupont, S., Wickstr\u00f6m, S.A.: Mechanical regulation of chromatin and transcription. Nat. Rev. Genet. 23(10), 624\u2013643 (2022)","journal-title":"Nat. Rev. Genet."},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41576-022-00509-1","volume":"24","author":"S Preissl","year":"2023","unstructured":"Preissl, S., Gaulton, K.J., Ren, B.: Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24(1), 21\u201343 (2023)","journal-title":"Nat. Rev. Genet."},{"issue":"5","key":"15_CR5","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1049\/cje.2021.06.003","volume":"30","author":"W Chao","year":"2021","unstructured":"Chao, W., Quan, Z.: A machine learning method for differentiating and predicting human-infective coronavirus based on physicochemical features and composition of the spike protein. Chin. J. Electron. 30(5), 815\u2013823 (2021)","journal-title":"Chin. J. Electron."},{"issue":"7345","key":"15_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/nature09906","volume":"473","author":"J Ernst","year":"2011","unstructured":"Ernst, J., et al.: Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473(7345), 43\u201349 (2011)","journal-title":"Nature"},{"issue":"3","key":"15_CR7","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1038\/s41588-022-01026-x","volume":"54","author":"Z Wang","year":"2022","unstructured":"Wang, Z., et al.: Prediction of histone post-translational modification patterns based on nascent transcription data. Nat. Genet. 54(3), 295\u2013305 (2022)","journal-title":"Nat. Genet."},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-021-02572-z","volume":"23","author":"H Vu","year":"2022","unstructured":"Vu, H., Ernst, J.: Universal annotation of the human genome through integration of over a thousand epigenomic datasets. Genome Biol. 23, 1\u201337 (2022)","journal-title":"Genome Biol."},{"issue":"10","key":"15_CR9","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou, J., Troyanskaya, O.G.: Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12(10), 931\u2013934 (2015)","journal-title":"Nat. Methods"},{"issue":"7","key":"15_CR10","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1038\/s41588-022-01102-2","volume":"54","author":"KM Chen","year":"2022","unstructured":"Chen, K.M., Wong, A.K., Troyanskaya, O.G., Zhou, J.: A sequence-based global map of regulatory activity for deciphering human genetics. Nat. Genet. 54(7), 940\u2013949 (2022)","journal-title":"Nat. Genet."},{"issue":"11","key":"15_CR11","doi-asserted-by":"publisher","first-page":"e107","DOI":"10.1093\/nar\/gkw226","volume":"44","author":"D Quang","year":"2016","unstructured":"Quang, D., Xie, X.: DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44(11), e107\u2013e107 (2016)","journal-title":"Nucleic Acids Res."},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Yao, Z., Zhang, W., Song, P., Hu, Y., Liu, J.: DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences. Briefings in Bioinformatics 24(2), bbad095 (2023)","DOI":"10.1093\/bib\/bbad095"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., Pu, Y., Tang, J., Zou, Q., Guo, F.: Deepatt: a hybrid category attention neural network for identifying functional effects of dna sequences. Briefings in bioinformatics 22(3), bbaa159 (2021)","DOI":"10.1093\/bib\/bbaa159"},{"issue":"7","key":"15_CR14","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1101\/gr.200535.115","volume":"26","author":"DR Kelley","year":"2016","unstructured":"Kelley, D.R., Snoek, J., Rinn, J.L.: Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26(7), 990\u2013999 (2016)","journal-title":"Genome Res."},{"key":"15_CR15","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Tan, J., et al.: Equalization loss for long-tailed object recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662\u201311671 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3268118"},{"key":"15_CR18","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Li, S., Gong, K., Liu, C.H., Wang, Y., Qiao, F., Cheng, X.: Metasaug: Meta semantic augmentation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern recognition, pp. 5212\u20135221 (2021)","DOI":"10.1109\/CVPR46437.2021.00517"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"15_CR21","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1x1 convolutions. Advances in neural Inf. Process. Syst. 31 (2018)"},{"key":"15_CR22","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Z., Yuan, H., Zhu, G., Zhang, Y.: Heap: a task adaptive-based explainable deep learning framework for enhancer activity prediction. Briefings in Bioinformatics, p. bbad286 (2023)","DOI":"10.1093\/bib\/bbad286"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"3","key":"15_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009941","volume":"18","author":"Q Zhang","year":"2022","unstructured":"Zhang, Q., et al.: Base-resolution prediction of transcription factor binding signals by a deep learning framework. PLoS Comput. Biol. 18(3), e1009941 (2022)","journal-title":"PLoS Comput. Biol."},{"issue":"11","key":"15_CR26","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.3390\/genes13111952","volume":"13","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., et al.: Uncovering the relationship between tissue-specific TF-DNA binding and chromatin features through a transformer-based model. Genes 13(11), 1952 (2022)","journal-title":"Genes"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8435-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:11:15Z","timestamp":1703319075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8435-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984343","9789819984350"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8435-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}