{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:47:26Z","timestamp":1743155246362,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031380785"},{"type":"electronic","value":"9783031380792"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-38079-2_10","type":"book-chapter","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T19:04:03Z","timestamp":1689015843000},"page":"97-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neoantigen Detection Using Transformers and\u00a0Transfer Learning in\u00a0the\u00a0Cancer Immunology Context"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8990-5495","authenticated-orcid":false,"given":"Vicente Enrique Machaca","family":"Arceda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Borden, E.S., Buetow, K.H., Wilson, M.A., Hastings, K.T.: Cancer neoantigens: challenges and future directions for prediction, prioritization, and validation. Front. Oncol. 12, (2022)","DOI":"10.3389\/fonc.2022.836821"},{"issue":"7","key":"10_CR2","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1080\/14760584.2021.1935248","volume":"20","author":"I Chen","year":"2021","unstructured":"Chen, I., Chen, M., Goedegebuure, P., Gillanders, W.: Challenges targeting cancer neoantigens in 2021: a systematic literature review. Expert Rev. Vaccines 20(7), 827\u2013837 (2021)","journal-title":"Expert Rev. Vaccines"},{"issue":"10","key":"10_CR3","doi-asserted-by":"publisher","first-page":"2879","DOI":"10.3390\/cancers12102879","volume":"12","author":"AV Gopanenko","year":"2020","unstructured":"Gopanenko, A.V., Kosobokova, E.N., Kosorukov, V.S.: Main strategies for the identification of neoantigens. Cancers 12(10), 2879 (2020)","journal-title":"Cancers"},{"issue":"8","key":"10_CR4","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.annonc.2020.05.008","volume":"31","author":"L Mattos","year":"2020","unstructured":"Mattos, L., et al.: Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO precision medicine working group. Ann. Oncol. 31(8), 978\u2013990 (2020)","journal-title":"Ann. Oncol."},{"issue":"1","key":"10_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12943-019-1055-6","volume":"18","author":"M Peng","year":"2019","unstructured":"Peng, M., et al.: Neoantigen vaccine: an emerging tumor immunotherapy. Mol. Cancer 18(1), 1\u201314 (2019)","journal-title":"Mol. Cancer"},{"issue":"W1","key":"10_CR6","doi-asserted-by":"publisher","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","volume":"48","author":"B Reynisson","year":"2020","unstructured":"Reynisson, B., Alvarez, B., Paul, S., Peters, B., Nielsen, M.: Netmhcpan-4.1 and Netmhciipan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48(W1), W449\u2013W454 (2020)","journal-title":"Nucleic Acids Res."},{"issue":"12","key":"10_CR7","doi-asserted-by":"publisher","first-page":"2459","DOI":"10.1074\/mcp.TIR119.001658","volume":"18","author":"B Alvarez","year":"2019","unstructured":"Alvarez, B., et al.: Nnalign_ma; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. Mol. Cell. Proteomics 18(12), 2459\u20132477 (2019)","journal-title":"Mol. Cell. Proteomics"},{"issue":"1","key":"10_CR8","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.cels.2020.06.010","volume":"11","author":"TJ O\u2019Donnell","year":"2020","unstructured":"O\u2019Donnell, T.J., Rubinsteyn, A., Laserson, U.: Mhcflurry 2.0: improved pan-allele prediction of MHC class i-presented peptides by incorporating antigen processing. Cell Syst. 11(1), 42\u201348 (2020)","journal-title":"Cell Syst."},{"issue":"4","key":"10_CR9","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1093\/bioinformatics\/btv639","volume":"32","author":"M Andreatta","year":"2016","unstructured":"Andreatta, M., Nielsen, M.: Gapped sequence alignment using artificial neural networks: application to the MHC class i system. Bioinformatics 32(4), 511\u2013517 (2016)","journal-title":"Bioinformatics"},{"issue":"4","key":"10_CR10","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/info14040242","volume":"14","author":"N Patwardhan","year":"2023","unstructured":"Patwardhan, N., Marrone, S., Sansone, C.: Transformers in the real world: a survey on NLP applications. Information 14(4), 242 (2023)","journal-title":"Information"},{"issue":"22","key":"10_CR11","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.1093\/bioinformatics\/btab422","volume":"37","author":"J Cheng","year":"2021","unstructured":"Cheng, J., Bendjama, K., Rittner, K., Malone, B.: BERTMHC: improved MHC-peptide class ii interaction prediction with transformer and multiple instance learning. Bioinformatics 37(22), 4172\u20134179 (2021)","journal-title":"Bioinformatics"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Rao, R., et al.: Evaluating protein transfer learning with tape. In: Advances in Neural Information Processing Systems, vol. 32, (2019)","DOI":"10.1101\/676825"},{"key":"10_CR13","unstructured":"Gasser, H.-C., Bedran, G., Ren, B., Goodlett, D., Alfaro, J., Rajan, A.: Interpreting BERT architecture predictions for peptide presentation by MHC class i proteins. arXiv preprintarXiv:2111.07137 (2021)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Mhcroberta: pan-specific peptide\u2013MHC class i binding prediction through transfer learning with label-agnostic protein sequences. Briefings Bioinf. 23(3), bab595 (022)","DOI":"10.1093\/bib\/bbab595"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: HLAB: learning the bilstm features from the protbert-encoded proteins for the class i hla-peptide binding prediction. Briefings Bioinf. (2022)","DOI":"10.1093\/bib\/bbac173"},{"issue":"D1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"D339","DOI":"10.1093\/nar\/gky1006","volume":"47","author":"R Vita","year":"2018","unstructured":"Vita, R., et al.: The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 47(D1), D339\u2013D343 (2018)","journal-title":"Nucleic Acids Res."},{"issue":"10","key":"10_CR17","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","volume":"44","author":"A Elnaggar","year":"2021","unstructured":"Elnaggar, A., et al.: Prottrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 7112\u20137127 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10_CR18","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1038\/s42256-022-00459-7","volume":"4","author":"Y Chu","year":"2022","unstructured":"Chu, Y., et al.: A transformer-based model to predict peptide-HLA class i binding and optimize mutated peptides for vaccine design. Nat. Mach. Intell. 4(3), 300\u2013311 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"10_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ab.2023.115075","volume":"666","author":"Y Jing","year":"2023","unstructured":"Jing, Y., Zhang, S., Wang, H.: DapNet-HLA: adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites. Anal. Biochem. 666, 115075 (2023)","journal-title":"Anal. Biochem."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Rives, A., et al.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. In: Proceedings of the National Academy of Sciences, vol. 118, no. 15, (2021)","DOI":"10.1073\/pnas.2016239118"},{"issue":"3","key":"10_CR21","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1111\/imm.12889","volume":"154","author":"KK Jensen","year":"2018","unstructured":"Jensen, K.K., et al.: Improved methods for predicting peptide binding affinity to MHC class ii molecules. Immunology 154(3), 394\u2013406 (2018)","journal-title":"Immunology"}],"container-title":["Lecture Notes in Networks and Systems","Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-38079-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T19:28:14Z","timestamp":1689017294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-38079-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031380785","9783031380792"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-38079-2_10","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PACBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Practical Applications of Computational Biology & Bioinformatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimaraes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"12 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pacbb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pacbb.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}