THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE

项目摘要 我们提案的主要目标是提高癌症表位数据库中数据的AI/ML准备程度， 分析资源（CEDAR）。CEDAR提供了一个从期刊文章中手工整理的信息目录 描述了适应性免疫反应的特异性分子靶点-也称为“表位”-在肿瘤中 细胞像生物医学领域的许多其他策展资源一样，CEDAR跟踪了 它通过识别期刊文章（通过其PubMed记录可以直接获取）和 文章中包含策展数据的特定部分。我们在这里建议使数据位置字段 在可用于AI/ML方法的CEDAR中，这将允许比较算法提取的信息 从自由文本文章到CEDAR中的策展数据。具体而言，我们将：1）标准化如何“数据定位”， 在CEDAR中捕获了一份手稿。2)以编程方式将数据位置链接到PubMed中的部分期刊文章 中央（PMC）。完成这些目标将使CEDAR数据对大型语言模型更有价值 (LLM)以及相关的AI应用。此外，开发的方法和代码将适用于许多 其他生物医学知识库，从文献中收集数据并捕捉其特定位置。

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.