{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T05:46:39Z","timestamp":1768974399123,"version":"3.49.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Initializing Fund of Xuzhou Medical University","award":["D2020040"],"award-info":[{"award-number":["D2020040"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32100998 32201016 JSSCBS20211265"],"award-info":[{"award-number":["32100998 32201016 JSSCBS20211265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Initializing Fund of Xuzhou Medical University","award":["D2020040"],"award-info":[{"award-number":["D2020040"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32100998 32201016 JSSCBS20211265"],"award-info":[{"award-number":["32100998 32201016 JSSCBS20211265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the continuous advancements in cancer immunotherapy, neoantigen-based therapies have demonstrated remarkable clinical efficacy. However, accurately predicting the immunogenicity of neoantigens remains a significant challenge. This is mainly due to two core factors: the scarcity of high-quality neoantigen datasets and the limited prediction accuracy of existing immunogenicity prediction tools. This study addressed these issues through several key steps. First, it collected and organized immunogenic neoantigen peptide data from publicly available literature and neoantigen databases. Second, it analyzed the data to identify key features influencing neoantigen immunogenicity prediction. Finally, it integrated existing prediction tools to create TumorAgDB1.0, a comprehensive tumor neoantigen database. TumorAgDB1.0 offers a user-friendly platform. Users can efficiently search for neoantigen data using parameters like amino acid sequence and peptide length. The platform also offers detailed information on the characteristics of neoantigens and tools for predicting tumor neoantigen immunogenicity. Additionally, the database includes a data download function, allowing researchers to easily access high-quality data to support the development and improvement of neoantigen immunogenicity prediction tools. In summary, TumorAgDB1.0 is a powerful tool for neoantigen screening and validation in tumor immunotherapy. It offers strong support to researchers.<\/jats:p>\n               <jats:p>Database URL: https:\/\/tumoragdb.com.cn<\/jats:p>","DOI":"10.1093\/database\/baaf010","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T07:20:06Z","timestamp":1738308006000},"source":"Crossref","is-referenced-by-count":2,"title":["TumorAgDB1.0: tumor neoantigen database platform"],"prefix":"10.1093","volume":"2025","author":[{"given":"Yan","family":"Shao","sequence":"first","affiliation":[{"name":"School of Medical Infand Engineering, Xuzhou Medical University , No. 209, Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004,","place":["China"]}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Histology and Embryology, Shantou University Medical College , No. 243, Daxue Road, Shantou, Guangdong 515063,","place":["China"]}]},{"given":"Ling-Yu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Medical Infand Engineering, Xuzhou Medical University , No. 209, Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004,","place":["China"]}]},{"given":"Shu-Guang","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Medical Infand Engineering, Xuzhou Medical University , No. 209, Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3017-0137","authenticated-orcid":false,"given":"Peng-Bo","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Medical Infand Engineering, Xuzhou Medical University , No. 209, Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"2025092510530867200_R1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.672356","article-title":"Neoantigen: a new breakthrough in tumor immunotherapy","volume":"12","author":"Zhang","year":"2021","journal-title":"Front 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