{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:03:57Z","timestamp":1742911437441,"version":"3.40.3"},"publisher-location":"Cham","reference-count":9,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9781071639887"},{"type":"electronic","value":"9781071639894"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-1-0716-3989-4_28","type":"book-chapter","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T08:02:38Z","timestamp":1715846558000},"page":"348-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Discovering and\u00a0Overcoming the\u00a0Bias in\u00a0Neoantigen Identification by\u00a0Unified Machine Learning Models"],"prefix":"10.1007","author":[{"given":"Ziting","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Wenxu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Xiaowo","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1038\/s42256-023-00694-6","volume":"5","author":"BA Albert","year":"2023","unstructured":"Albert, B.A., et al.: Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat. Mach. Intell. 5, 861\u2013872 (2023)","journal-title":"Nat. Mach. Intell."},{"issue":"3","key":"28_CR2","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":"28_CR3","doi-asserted-by":"crossref","unstructured":"Gfeller, D., et al.: Improved predictions of antigen presentation and TCR recognition with MixMHCpred2. 2 and PRIME2. 0 reveal potent SARS-CoV-2 CD8+ t-cell epitopes. Cell Syst. 14(1), 72\u201383 (2023)","DOI":"10.1016\/j.cels.2022.12.002"},{"issue":"2","key":"28_CR4","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/s41588-022-01273-y","volume":"55","author":"JY Kim","year":"2023","unstructured":"Kim, J.Y., et al.: MHC II immunogenicity shapes the neoepitope landscape in human tumors. Nat. Genet. 55(2), 221\u2013231 (2023)","journal-title":"Nat. Genet."},{"issue":"4","key":"28_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41573-021-00387-y","volume":"21","author":"F Lang","year":"2022","unstructured":"Lang, F., Schr\u00f6rs, B., L\u00f6wer, M., T\u00fcreci, \u00d6., Sahin, U.: Identification of neoantigens for individualized therapeutic cancer vaccines. Nat. Rev. Drug Discov. 21(4), 261\u2013282 (2022)","journal-title":"Nat. Rev. Drug Discov."},{"issue":"7378","key":"28_CR6","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/nature10673","volume":"480","author":"I Mellman","year":"2011","unstructured":"Mellman, I., Coukos, G., Dranoff, G.: Cancer immunotherapy comes of age. Nature 480(7378), 480\u2013489 (2011)","journal-title":"Nature"},{"key":"28_CR7","unstructured":"Qiao, Y., Xiong, C., Liu, Z., Liu, Z.: Understanding the behaviors of BERT in ranking. arXiv preprint arXiv:1904.07531 (2019)"},{"key":"28_CR8","doi-asserted-by":"crossref","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)","DOI":"10.1093\/nar\/gkaa379"},{"issue":"3","key":"28_CR9","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1016\/j.cell.2020.09.015","volume":"183","author":"DK Wells","year":"2020","unstructured":"Wells, D.K., et al.: Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183(3), 818\u2013834 (2020)","journal-title":"Cell"}],"container-title":["Lecture Notes in Computer Science","Research in Computational Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-3989-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T08:05:13Z","timestamp":1715846713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-1-0716-3989-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9781071639887","9781071639894"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-3989-4_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RECOMB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Research in Computational Molecular Biology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge, MA","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 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"recomb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/recomb.org\/recomb2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}