{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:43:47Z","timestamp":1742957027313,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138690"},{"type":"electronic","value":"9783031138706"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-13870-6_65","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T09:03:13Z","timestamp":1660467793000},"page":"799-810","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Deep Learning to Predict Transcription Factor Binding Sites Based on Multiple-omics Data"],"prefix":"10.1007","author":[{"given":"Youhong","family":"Xu","sequence":"first","affiliation":[]},{"given":"Changan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Hongjie","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xingming","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"issue":"2","key":"65_CR1","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.cell.2018.09.045","volume":"175","author":"SA Lambert","year":"2018","unstructured":"Lambert, S.A., et al.: The human transcription factors. Cell 175(2), 598\u2013599 (2018)","journal-title":"Cell"},{"issue":"1","key":"65_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-020-01138-0","volume":"11","author":"JR Teixeira","year":"2021","unstructured":"Teixeira, J.R., Szeto, R.A., Carvalho, V.M.A., et al.: Transcription factor 4 and its association with psychiatric disorders. Transl. Psychiatry 11(1), 1\u201312 (2021)","journal-title":"Transl. Psychiatry"},{"key":"65_CR3","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.10845","volume":"9","author":"Q Wu","year":"2021","unstructured":"Wu, Q., Li, W., You, C.: The regulatory roles and mechanisms of the transcription factor FOXF2 in human diseases. PeerJ 9, e10845 (2021)","journal-title":"PeerJ"},{"key":"65_CR4","doi-asserted-by":"crossref","unstructured":"Tianyin, Z., Ning, S., et al. Quantitative modeling of transcription factor binding specificities using DNA shape. In: Proceedings of the National Academy of Sciences, pp. 112\u2013115 (2015)","DOI":"10.1073\/pnas.1422023112"},{"issue":"1","key":"65_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/nmeth1156","volume":"5","author":"SC Schuster","year":"2008","unstructured":"Schuster, S.C.: Next-generation sequencing transforms today\u2019s biology. Nat. Methods 5(1), 16\u201318 (2008)","journal-title":"Nat. Methods"},{"issue":"11","key":"65_CR6","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1038\/nrg2845","volume":"11","author":"GD Stormo","year":"2010","unstructured":"Stormo, G.D., Zhao, Y.: Determining the specificity of protein\u2013DNA interactions. Nat. Rev. Genet. 11(11), 751\u2013760 (2010)","journal-title":"Nat. Rev. Genet."},{"issue":"9","key":"65_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0024210","volume":"6","author":"Y Bi","year":"2011","unstructured":"Bi, Y., Kim, H., Gupta, R., et al.: Tree-based position weight matrix approach to model transcription factor binding site profiles. PLoS One 6(9), e24210 (2011)","journal-title":"PLoS One"},{"issue":"9","key":"65_CR8","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1089\/cmb.2012.0289","volume":"20","author":"E Giaquinta","year":"2013","unstructured":"Giaquinta, E., Grabowski, S., Ukkonen, E.: Fast matching of transcription factor motifs using generalized position weight matrix models. J. Comput. Biol. 20(9), 621\u2013630 (2013)","journal-title":"J. Comput. Biol."},{"issue":"W1","key":"65_CR9","doi-asserted-by":"publisher","first-page":"W544","DOI":"10.1093\/nar\/gkt519","volume":"41","author":"C Fletez-Brant","year":"2013","unstructured":"Fletez-Brant, C., Lee, D., McCallion, A.S., et al.: kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Res. 41(W1), W544\u2013W556 (2013)","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"65_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003711","volume":"10","author":"M Ghandi","year":"2014","unstructured":"Ghandi, M., Lee, D., Mohammad-Noori, M., et al.: Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10(7), e1003711 (2014)","journal-title":"PLoS Comput. Biol."},{"issue":"14","key":"65_CR11","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1093\/bioinformatics\/btw142","volume":"32","author":"D Lee","year":"2016","unstructured":"Lee, D.: LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32(14), 2196\u20132198 (2016)","journal-title":"Bioinformatics"},{"key":"65_CR12","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831\u2013838 (2015)","journal-title":"Nat. Biotechnol."},{"issue":"10","key":"65_CR13","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"Z Jian","year":"2015","unstructured":"Jian, Z., 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":"2","key":"65_CR14","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TCBB.2018.2864203","volume":"17","author":"Q Zhang","year":"2020","unstructured":"Zhang, Q., Zhu, L., Bao, W., Huang, D.-S.: Weakly-supervised convolutional neural network architecture for predicting protein-DNA binding. IEEE\/ACM Trans. Comput. Biol. Bioinform. 17(2), 679\u2013689 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"4","key":"65_CR15","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1109\/TCBB.2018.2819660","volume":"16","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Zhu, L., Huang, D.-S.: High-order convolutional neural network architecture for predicting DNA-protein binding sites. IEEE\/ACM Trans. Comput. Biol. Bioinform. 16(4), 1184\u20131192 (2019)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"1","key":"65_CR16","doi-asserted-by":"publisher","first-page":"8484","DOI":"10.1038\/s41598-019-44966-x","volume":"9","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Shen, Z., Huang, D.-S.: Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network. Sci Rep. 9(1), 8484 (2019)","journal-title":"Sci Rep."},{"issue":"6","key":"65_CR17","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1109\/TCBB.2016.2561930","volume":"15","author":"H Zhang","year":"2018","unstructured":"Zhang, H., Zhu, L., Huang, D.S.: DiscMLA: an efficient discriminative motif learning algorithm over high-throughput datasets. IEEE\/ACM Trans. Comput. Biol. Bioinform. 15(6), 1810\u20131820 (2018)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"3","key":"65_CR18","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/TCBB.2017.2691325","volume":"15","author":"L Zhu","year":"2018","unstructured":"Zhu, L., Zhang, H., Huang, D.S.: LMMO: a large margin approach for optimizing regulatory motifs. IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB) 15(3), 913\u2013925 (2018)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB)"},{"key":"65_CR19","doi-asserted-by":"publisher","first-page":"i639","DOI":"10.1093\/bioinformatics\/btw427","volume":"32","author":"S Ritambhara","year":"2016","unstructured":"Ritambhara, S., Lanchantin, J., et al.: DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics 32, i639\u2013i648 (2016)","journal-title":"Bioinformatics"},{"issue":"2","key":"65_CR20","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1038\/nbt.2486","volume":"31","author":"MT Weirauch","year":"2013","unstructured":"Weirauch, M.T., Cote, A., Norel, R., et al.: Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31(2), 126\u2013134 (2013)","journal-title":"Nat. Biotechnol."},{"issue":"2","key":"65_CR21","first-page":"1137","volume":"14","author":"R Kohavi","year":"1995","unstructured":"Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14(2), 1137\u20131145 (1995)","journal-title":"IJCAI"},{"key":"65_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., Lee, D.L.: Billion-scale commodity embedding for E-commerce recommendation in Alibaba. In: Knowledge Discovery and Data Mining, pp. 839\u2013848 (2018)","DOI":"10.1145\/3219819.3219869"},{"key":"65_CR23","doi-asserted-by":"crossref","unstructured":"Zhu, L., Guo, W.-L., Huang, D.-S., Lu, C.-Y.: Imputation of ChIP-seq datasets via low rank convex co-embedding. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 141\u2013144 (2015)","DOI":"10.1109\/BIBM.2015.7359671"},{"key":"65_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/978-3-030-26969-2_36","volume-title":"Intelligent Computing Theories and Application","author":"D Wang","year":"2019","unstructured":"Wang, D., Zhang, Q., Yuan, C.-A., Qin, X., Huang, Z.-K., Shang, L.: Motif discovery via convolutional networks with K-mer embedding. In: Huang, D.-S., Jo, K.-H., Huang, Z.-K. (eds.) ICIC 2019. LNCS, vol. 11644, pp. 374\u2013382. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26969-2_36"},{"key":"65_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, L., Guo, W.-L., Huang, D.-S., Lu, C.-Y.: Imputation of ChIP-seq datasets via Low Rank Convex Co-Embedding. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 141\u2013144 (2015)","DOI":"10.1109\/BIBM.2015.7359671"},{"issue":"2","key":"65_CR26","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TNB.2019.2891239","volume":"18","author":"X Wenxuan","year":"2019","unstructured":"Wenxuan, X., Zhu, L., Huang, D.-S.: DCDE: an efficient deep convolutional divergence encoding method for human promoter recognition. IEEE Trans. Nanobiosci. 18(2), 136\u2013145 (2019)","journal-title":"IEEE Trans. Nanobiosci."},{"issue":"2","key":"65_CR27","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TCBB.2019.2947461","volume":"18","author":"Q Zhang","year":"2021","unstructured":"Zhang, Q., Shen, Z., Huang, D.-S.: Predicting in-vitro transcription factor binding sites using DNA sequence + shape. IEEE\/ACM Trans. Comput. Biol. Bioinform. 18(2), 667\u2013676 (2021)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"4","key":"65_CR28","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.1109\/JBHI.2021.3117616","volume":"26","author":"S Wang","year":"2022","unstructured":"Wang, S., He, Y., Chen, Z., Zhang, Q.: FCNGRU: locating transcription factor binding sites by combing fully convolutional neural network with gated recurrent unit. IEEE J. Biomed. Health Inform. 26(4), 1883\u20131890 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"65_CR29","doi-asserted-by":"crossref","unstructured":"Shen, Z., Zhang, Q., Han, K., Huang, D.-S.: A deep learning model for RNA-protein binding preference prediction based on hierarchical LSTM and attention network. IEEE\/ACM Trans. Comput. Biol. Bioinform 19(2), 753\u2013762","DOI":"10.1109\/TCBB.2020.3007544"},{"issue":"5","key":"65_CR30","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TCBB.2019.2943465","volume":"17","author":"Z Shen","year":"2020","unstructured":"Shen, Z., Deng, S.-P., Huang, D.-S.: Capsule network for predicting RNA-protein binding preferences using hybrid feature. IEEE\/ACM Trans. Comput. Biol. Bioinform. 17(5), 1483\u20131492 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"5","key":"65_CR31","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1109\/TCBB.2019.2910513","volume":"17","author":"Z Shen","year":"2020","unstructured":"Shen, Z., Deng, S.-P., Huang, D.-S.: RNA-protein binding sites prediction via multi scale convolutional gated recurrent unit networks. IEEE\/ACM Trans. Comput. Biol. Bioinform. 17(5), 1741\u20131750 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"1","key":"65_CR32","doi-asserted-by":"publisher","first-page":"15270","DOI":"10.1038\/s41598-018-33321-1","volume":"8","author":"Z Shen","year":"2018","unstructured":"Shen, Z., Bao, W., Huang, D.-S.: Recurrent neural network for predicting transcription factor binding sites. Sci. Rep. 8(1), 15270 (2018)","journal-title":"Sci. Rep."},{"issue":"2017","key":"65_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/2498957","volume":"2017","author":"Z Shen","year":"2017","unstructured":"Shen, Z., Zhang, Y.-H., Han, K., Nandi, A.K., Honig, B., Huang, D.-S.: miRNA-disease association prediction with collaborative matrix factorization. Complexity 2017(2017), 1\u20139 (2017)","journal-title":"Complexity"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13870-6_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T18:10:00Z","timestamp":1727806200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13870-6_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138690","9783031138706"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13870-6_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","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":"209","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":"47% - 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","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":"2.5","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)"}}]}}