{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:46:06Z","timestamp":1743075966142,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031755989"},{"type":"electronic","value":"9783031755996"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-75599-6_11","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:16:25Z","timestamp":1729887385000},"page":"148-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Vaxign-DL for\u00a0Vaccine Candidate Prediction with\u00a0Added ESM-Generated Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5758-7349","authenticated-orcid":false,"given":"Yichao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7056-3331","authenticated-orcid":false,"given":"Yuhan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9189-9661","authenticated-orcid":false,"given":"Yongqun","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Huffman, A., et al.: COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. Brief. Bioinform. 23(4), bbac190 (2022). https:\/\/doi.org\/10.1093\/bib\/bbac190","DOI":"10.1093\/bib\/bbac190"},{"key":"11_CR2","doi-asserted-by":"publisher","unstructured":"He, Y., et al.: Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J. Biomed. Biotechnol. 2010, 297505 (2010). https:\/\/doi.org\/10.1155\/2010\/297505","DOI":"10.1155\/2010\/297505"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huffman, A., Johnson, J., He, Y.: Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation. bioRxiv (2023). https:\/\/doi.org\/10.1101\/2023.11.29.569096v1","DOI":"10.1101\/2023.11.29.569096"},{"issue":"10","key":"11_CR4","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1093\/bioinformatics\/btaa119","volume":"36","author":"E Ong","year":"2020","unstructured":"Ong, E., Wang, H., Wong, M.U., Seetharaman, M., Valdez, N., He, Y.: Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics 36(10), 3185\u20133191 (2020). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa119","journal-title":"Bioinformatics"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Jumper, J., Evans, R., Pritzel, A., et al.: Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589 (2021). https:\/\/doi.org\/10.1038\/s41586-021-03819-2","DOI":"10.1038\/s41586-021-03819-2"},{"issue":"21","key":"11_CR6","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1073\/pnas.2016239118","volume":"117","author":"R Yan","year":"2020","unstructured":"Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., Zhou, Q.: Molecular architecture and conformational dynamics of SARS-CoV-2 spike protein in complex with ACE2 receptor. Proc. Natl. Acad. Sci. 117(21), 2020\u20132025 (2020). https:\/\/doi.org\/10.1073\/pnas.2016239118","journal-title":"Proc. Natl. Acad. Sci."},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"Yang, B., Sayers, S., Xiang, Z., He, Y.: Protegen: a web-based protective antigen database and analysis system. Nucleic Acids Res. 39(Database issue), D1073\u2013D1078 (2011). https:\/\/doi.org\/10.1093\/nar\/gkq944 PMID: 20959289; PMCID: PMC3013795","DOI":"10.1093\/nar\/gkq944"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Bowman, B.N., et al.: Improving reverse vaccinology with a machine learning approach. Vaccine 29(45), 8156\u20138164 (2011). https:\/\/doi.org\/10.1016\/j.vaccine.2011.07.142","DOI":"10.1016\/j.vaccine.2011.07.142"},{"issue":"5","key":"11_CR9","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw."},{"key":"11_CR10","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning (ICML), vol. 30, no. 1 (2013)"},{"key":"11_CR11","doi-asserted-by":"publisher","unstructured":"Lin, Z., et al.: Evolutionary-scale prediction of atomic level protein structure with a language model. Originally published in bioRxiv. https:\/\/doi.org\/10.1101\/2022.07.20.500902. Now published in Science. https:\/\/doi.org\/10.1126\/science.ade2574","DOI":"10.1126\/science.ade2574"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Rives, A., Meier, J., Sercu, T., Fergus, R., et al.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. bioRxiv (2021). https:\/\/doi.org\/10.1101\/2020.11.20.391069","DOI":"10.1101\/2020.11.20.391069"},{"issue":"8","key":"11_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Tiessen, A., P\u00e9rez-Rodr\u00edguez, P., Delaye-Arredondo, L.J.: Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes. BMC. Res. Notes 5, 85 (2012). https:\/\/doi.org\/10.1186\/1756-0500-5-85, PMID: 22296664; PMCID: PMC3296660","DOI":"10.1186\/1756-0500-5-85"}],"container-title":["Lecture Notes in Computer Science","Advances in Conceptual Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75599-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:18:26Z","timestamp":1729887506000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75599-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031755989","9783031755996"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75599-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ER","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Conceptual Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pittsburg, PA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"er2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/resources.sei.cmu.edu\/news-events\/events\/er2024\/cfp.cfm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}