{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:38Z","timestamp":1760245058423,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030914141"},{"type":"electronic","value":"9783030914158"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91415-8_18","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T18:04:03Z","timestamp":1637172243000},"page":"203-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["BindTransNet: A Transferable Transformer-Based Architecture for Cross-Cell Type DNA-Protein Binding Sites Prediction"],"prefix":"10.1007","author":[{"given":"Zixuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beichen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Yang","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":[[2021,11,18]]},"reference":[{"issue":"4","key":"18_CR1","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.cell.2018.01.029","volume":"172","author":"L Samuel","year":"2018","unstructured":"Samuel, L., Arttu, J., Laura, C., et al.: The human transcription factors. Cell 172(4), 650\u2013665 (2018)","journal-title":"Cell"},{"issue":"9","key":"18_CR2","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.tibs.2014.07.002","volume":"39","author":"S Matthew","year":"2014","unstructured":"Matthew, S., Tianyin, Z., Lin, Y., et al.: Absence of a simple code: how transcription factors read the genome. Trends in biochemical sciences 39(9), 381\u2013399 (2014)","journal-title":"Trends in biochemical sciences"},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.cels.2016.07.001","volume":"3","author":"M Anthony","year":"2016","unstructured":"Anthony, M., Beibei, X., Tsu-Pei, C., et al.: Dna shape features improve transcription factor binding site predictions in vivo. Cell systems 3(3), 278\u2013286 (2016)","journal-title":"Cell systems"},{"issue":"2","key":"18_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s40484-013-0012-4","volume":"1","author":"G Stormo","year":"2013","unstructured":"Stormo, G.: Modeling the specificity of protein-dna interactions. Quantitative biology 1(2), 115\u2013130 (2013)","journal-title":"Quantitative biology"},{"key":"18_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.ymeth.2019.04.008","volume":"166","author":"L Yu","year":"2019","unstructured":"Yu, L., Chao, H., Lizhong, D., et al.: Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166, 4\u201321 (2019)","journal-title":"Methods"},{"key":"18_CR6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.engappai.2019.01.003","volume":"79","author":"Z Yongqing","year":"2019","unstructured":"Yongqing, Z., Shaojie, Q., Shengjie, J., et al.: Identification of dna-protein binding sites by bootstrap multiple convolutional neural networks on sequence information. Engineering Applications of Artificial Intelligence 79, 58\u201366 (2019)","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"4","key":"18_CR7","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s13042-019-00990-x","volume":"11","author":"Z Yongqing","year":"2020","unstructured":"Yongqing, Z., Shaojie, Q., Shengjie, J., et al.: Deepsite: bidirectional lstm and cnn models for predicting dna-protein binding. International Journal of Machine Learning and Cybernetics 11(4), 841\u2013851 (2020)","journal-title":"International Journal of Machine Learning and Cybernetics"},{"issue":"8","key":"18_CR8","first-page":"898","volume":"15","author":"Z Yongqing","year":"2020","unstructured":"Yongqing, Z., Jianrong, Y., Siyu, C., et al.: Review of the applications of deep learning in bioinformatics. Current Bioinformatics 15(8), 898\u2013911 (2020)","journal-title":"Current Bioinformatics"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Yongqing, Z., Shaojie, Q., Yuanqi, Z., et\u00a0al.: Cae-cnn: Predicting transcription factor binding site with convolutional autoencoder and convolutional neural network. Expert Systems with Applications 183, 115404 (2021)","DOI":"10.1016\/j.eswa.2021.115404"},{"issue":"8","key":"18_CR10","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"A Babak","year":"2015","unstructured":"Babak, A., Andrew, D., Matthew, W., et al.: Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nature biotechnology 33(8), 831\u2013838 (2015)","journal-title":"Nature biotechnology"},{"issue":"10","key":"18_CR11","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"Z Jian","year":"2015","unstructured":"Jian, Z., Olga, T.: Predicting effects of noncoding variants with deep learning-based sequence model. Nature methods 12(10), 931\u2013934 (2015)","journal-title":"Nature methods"},{"issue":"11","key":"18_CR12","doi-asserted-by":"publisher","first-page":"e107","DOI":"10.1093\/nar\/gkw226","volume":"44","author":"Q Daniel","year":"2016","unstructured":"Daniel, Q., Xiaohui, X.: Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic acids research 44(11), e107\u2013e107 (2016)","journal-title":"Nucleic acids research"},{"issue":"11","key":"18_CR13","doi-asserted-by":"publisher","first-page":"5521","DOI":"10.3390\/ijms22115521","volume":"22","author":"L Deng","year":"2021","unstructured":"Deng, L., Wu, H., Liu, X., et al.: Deepd2v: A novel deep learning-based framework for predicting transcription factor binding sites from combined dna sequence. International journal of molecular sciences 22(11), 5521 (2021)","journal-title":"International journal of molecular sciences"},{"issue":"2","key":"18_CR14","first-page":"679","volume":"17","author":"Z Qinhu","year":"2018","unstructured":"Qinhu, Z., Lin, Z., Wenzheng, B., et al.: Weakly-supervised convolutional neural network architecture for predicting protein-dna binding. IEEE\/ACM transactions on computational biology and bioinformatics 17(2), 679\u2013689 (2018)","journal-title":"IEEE\/ACM transactions on computational biology and bioinformatics"},{"key":"18_CR15","unstructured":"Fang, J., Shaowu, Z., Zhen, C., et al.: An integrative framework for combining sequence and epigenomic data to predict transcription factor binding sites using deep learning. IEEE\/ACM transactions on computational biology and bioinformatics (2019)"},{"issue":"20","key":"18_CR16","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1093\/bioinformatics\/bty383","volume":"34","author":"S Sirajul","year":"2018","unstructured":"Sirajul, S., Jianqiu, Z., Yufei, H.: Base-pair resolution detection of transcription factor binding site by deep deconvolutional network. Bioinformatics 34(20), 3446\u20133453 (2018)","journal-title":"Bioinformatics"},{"issue":"24","key":"18_CR17","doi-asserted-by":"publisher","first-page":"5067","DOI":"10.1093\/bioinformatics\/btz451","volume":"35","author":"J Zhou","year":"2019","unstructured":"Zhou, J., Lu, Q., Gui, L., et al.: Mttfsite: cross-cell type tf binding site prediction by using multi-task learning. Bioinformatics 35(24), 5067\u20135077 (2019)","journal-title":"Bioinformatics"},{"issue":"1","key":"18_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"S Park","year":"2020","unstructured":"Park, S., Koh, Y., Jeon, H., et al.: Enhancing the interpretability of transcription factor binding site prediction using attention mechanism. Scientific reports 10(1), 1\u201310 (2020)","journal-title":"Scientific reports"},{"issue":"5","key":"18_CR19","first-page":"1445","volume":"15","author":"W Hongjie","year":"2017","unstructured":"Hongjie, W., Chengyuan, C., Xiaoyan, X., et al.: Unified deep learning architecture for modeling biology sequence. IEEE\/ACM transactions on computational biology and bioinformatics 15(5), 1445\u20131452 (2017)","journal-title":"IEEE\/ACM transactions on computational biology and bioinformatics"},{"key":"18_CR20","unstructured":"Ashish, V., Noam, S., Niki, P., et al.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998\u20136008 (2017)"},{"issue":"10","key":"18_CR21","first-page":"1345","volume":"22","author":"PS Jialin","year":"2009","unstructured":"Jialin, P.S., Qiang, Y.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Transactions on knowledge and data engineering"},{"issue":"12","key":"18_CR22","doi-asserted-by":"publisher","first-page":"i121","DOI":"10.1093\/bioinformatics\/btw255","volume":"32","author":"Z Haoyang","year":"2016","unstructured":"Haoyang, Z., Matthew, E., Ge, L.: other: Convolutional neural network architectures for predicting dna-protein binding. Bioinformatics 32(12), i121\u2013i127 (2016)","journal-title":"Bioinformatics"},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"219256","DOI":"10.1109\/ACCESS.2020.3042903","volume":"8","author":"Z Yuanqi","year":"2020","unstructured":"Yuanqi, Z., Meiqin, G., Meng, L., et al.: A review about transcription factor binding sites prediction based on deep learning. IEEE Access 8, 219256\u2013219274 (2020)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91415-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T04:52:58Z","timestamp":1637211178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91415-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030914141","9783030914158"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91415-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/alan.cs.gsu.edu\/isbra21\/?q=node\/1","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"51","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":"38% - 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":"2.97","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":"2.95","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}