Curation at scale: Integrating AI into community curation

项目摘要 生物知识库是研究人员的重要资源，并通过以下方式加速科学发现 提供人工管理的、机器可读的数据收集。然而，聚合和手动管理 生物数据的处理是一个劳动密集型的过程，几乎完全依赖于专业的生物计算器。二 已经提出了一些方法来帮助解决这个问题：自然语言处理(NLP；文本挖掘(TM)) 和机器学习(ML))和研究人员的参与(社区管理)。然而，这两个都不是 仅靠方法就足以满足提高生物固化过程效率的迫切需要。我们的 这些挑战的解决方案是一个NLP增强的社区管理门户，作者认证到知识库 (确认)。承认制度，目前在线虫文献中实施，夫妇 统计方法和文本挖掘算法，以增强研究文章的社区管理。我们建议 通过将其他物种纳入我们的管道来加强和扩大认识，纳入更多 复杂的机器学习模型，并提供句子级实体和概念提取，以获得更多 详细的作者策划。此外，我们将开发一个作者认证门户(ACP)，以允许作者轻松地 上传和管理他们自己的文档。综合起来，这些增强功能将使我们能够最大限度地 通过利用作者在生物学多个领域的专业知识进行社区管理工作，同时 为作者提供尽可能多的人工智能辅助管理。这种互惠互动将不会得到改善 只有知识库的内容，而是人工智能方法本身，因为我们将收到关于我们的 模特们。通过开发作者管理门户，我们将进一步授权作者参与管理 处理关键信息并向知识库发出警报，这些信息可以而且应该是容易发现的 具有公平(可查找、可访问、可互操作和可重用)数据原则。

Project Summary Biological knowledgebases are a critical resource for researchers and accelerate scientific discoveries by providing manually curated, machine-readable data collections. However, the aggregation and manual curation of biological data is a labor-intensive process that relies almost entirely on professional biocurators. Two approaches have been advanced to help with this problem: natural language processing (NLP; text mining (TM) and machine learning (ML)) and engagement of researchers (community curation). However, neither of these approaches alone is sufficient to address the critical need for increased efficiency in the biocuration process. Our solution to these challenges is an NLP-enhanced community curation portal, Author Curation to Knowledgebase (ACKnowledge). The ACKnowledge system, currently implemented for the C. elegans literature, couples statistical methods and text mining algorithms to enhance community curation of research articles. We propose to strengthen and expand ACKnowledge by including other species into our pipeline, incorporating more sophisticated machine learning models, and presenting sentence-level entity and concept extraction for more detailed author curation. In addition, we will develop an Author Curation Portal (ACP) to allow authors to easily upload and curate their own documents. Taken together, these enhancements will allow us to maximize community curation efforts by leveraging author expertise in multiple areas of biology, while at the same time supporting authors with as much AI-assisted curation as possible. This reciprocal interaction will improve not only the content of knowledgebases, but the AI methods themselves, as we will receive valuable feedback on our models. By developing an Author Curation Portal, we will further empower authors to participate in the curation process and alert knowledgebases to key information that can, and should, be readily discoverable in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.