THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE

癌症表位数据库和分析资源

• 批准号: 10842172
• 负责人: Bjoern Peters
• 金额: $ 36.6万
• 依托单位: LA JOLLA INSTITUTE FOR IMMUNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10842172
• 关键词: Abbreviations; Adopted; Algorithms; Artificial Intelligence; Award; Catalogs; Code; Communities; Consumption; Data; Data Reporting; Data Set; Data Sources; Databases; Deposition; Epitopes; Extensible Markup Language; Feedback; Funding; Future; Goals; Journals; Knowledge Extraction; Language; Letters; Link; Literature; Location; Machine Learning; Malignant Neoplasms; Manuals; Manuscripts; Methods; Modeling; Molecular Target; Outcome; Performance; Policies; Provider; PubMed; Readability; Readiness; Records; Resources; Science; Specific qualifier value; Standardization; System; Testing; Text; Time; Training; United States National Institutes of Health; Validation; Work; Writing; access restrictions; adaptive immune response; data resource; data standards; high standard; improved; journal article; knowledgebase; neoplastic cell; spelling; text searching; timeline; web site

Project Summary The primary goal of our proposal is to improve the AI/ML-readiness of data in the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR provides a catalog of manually curated information from journal articles that describe the specific molecular targets of adaptive immune responses - also called `epitopes' - in tumor cells. Like many other curated resources in the biomedical domain, CEDAR tracks the provenance of the statements it captures by identifying the journal article (which is straightforward by its PubMed record) and the specific part of the article where the curated data is contained. We here propose to make the data location field in CEDAR accessible for AI/ML approaches, that will allow the comparison of information extracted by algorithms from a free text article to the curated data in CEDAR. Specifically, we will: 1) Standardize how `data location' in a manuscript is captured in CEDAR. 2) Programmatically link data locations to parts of journal articles in PubMed Central (PMC). Completing these Aims will make the CEDAR data more valuable for large language models (LLM) and related AI applications. Moreover, the approaches and code developed will be applicable to the many other biomedical knowledgebases that curate data from the literature and capture its specific location.

项目摘要 我们建议的主要目标是改善癌症表位数据库中数据的AI/ML准备情况，并 分析资源(雪松)。Cedar提供了一份来自期刊文章的人工整理信息目录 描述了肿瘤中获得性免疫反应的特定分子靶点--也称为‘表位’ 细胞。像生物医学领域的许多其他精选资源一样，雪松追踪 它通过标识期刊文章(由其PubMed记录直接表示)和 文章中包含精选数据的特定部分。我们在这里建议将数据位置字段 在AI/ML方法可访问的雪松中，这将允许比较由算法提取的信息 从一篇自由文本文章到雪松的精选数据。具体地说，我们将：1)标准化如何在 在雪松捕获了一份手稿。2)以编程方式将数据位置链接到PubMed中的期刊文章的部分 中环(PMC)。完成这些目标将使雪松数据对大型语言模型更有价值 (LLM)和相关的人工智能应用程序。此外，开发的方法和代码将适用于许多人 其他生物医学知识库，精选文献中的数据并捕捉其特定位置。

项目成果

IEDB and CEDAR: Two Sibling Databases to Serve the Global Scientific Community.

IEDB 和 CEDAR：为全球科学界服务的两个兄弟数据库。

• DOI: 10.1007/978-1-0716-3239-0_9
• 发表时间: 2023
• 期刊: Methods in molecular biology (Clifton, N.J.)
• 作者: Blazeska,Nina;Kosaloglu-Yalcin,Zeynep;Vita,Randi;Peters,Bjoern;Sette,Alessandro
• 通讯作者: Sette,Alessandro

Estimating tissue-specific peptide abundance from public RNA-Seq data.

• DOI: 10.3389/fgene.2023.1082168
• 发表时间: 2023
• 期刊: FRONTIERS IN GENETICS
• 影响因子: 3.7
• 作者: Frentzen, Angela;Greenbaum, Jason A.;Kim, Haeuk;Peters, Bjoern;Kosaloglu-Yalcin, Zeynep
• 通讯作者: Kosaloglu-Yalcin, Zeynep

Evaluating the Bioenergetics Health Index Ratio in Leigh Syndrome Fibroblasts to Understand Disease Severity.

• DOI: 10.3390/ijms221910344
• 发表时间: 2021-09-26
• 期刊: International journal of molecular sciences
• 影响因子: 5.6
• 作者: Bakare AB;Dean J;Chen Q;Thorat V;Huang Y;LaFramboise T;Lesnefsky EJ;Iyer S
• 通讯作者: Iyer S

The role of antigen expression in shaping the repertoire of HLA presented ligands.

• DOI: 10.1016/j.isci.2022.104975
• 发表时间: 2022-09-16
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Alvarez, Heli M. Garcia;Kosaloglu-Yalcin, Zeynep;Peters, Bjoern;Nielsen, Morten
• 通讯作者: Nielsen, Morten

Predicting the Success of Fmoc-Based Peptide Synthesis.

• DOI: 10.1021/acsomega.2c02425
• 发表时间: 2022-07-12
• 期刊: ACS OMEGA
• 影响因子: 4.1
• 作者: Gutman, Ilanit;Gutman, Ron;Sidney, John;Chihab, Leila;Mishto, Michele;Liepe, Juliane;Chiem, Anthony;Greenbaum, Jason;Yan, Zhen;Sette, Alessandro;Kosaloglu-Yalcin, Zeynep;Peters, Bjoern
• 通讯作者: Peters, Bjoern