Natural language processing for automated validation of protein databases

该项目旨在利用自然语言处理和信息检索来协调和改进生物信息来源。生物学研究已经产生了大量关于蛋白质的信息，这些信息被捕获在结构化资源（数据库）和非结构化文档中。然而，这些信息的准确性值得怀疑。该项目建议开发方法来验证数据，并通过利用观察到的蛋白质信息资源之间的相关性和互补性，特别是通过从生物医学文献中提取有针对性的事实，减少蛋白质信息资源中的显著不一致。这些方法将在数百万篇已发表的文章中大规模应用，以推断和验证功能信息。

The project aims to use natural language processing and information retrieval to reconcile and improve sources of biological information. Biological research has produced vast volumes of information about proteins, captured in structured resources (databases) and unstructured documents. However, the accuracy of much of this information is questionable. The project proposes to develop methods to validate data and reduce the dramatic inconsistencies in protein information resources by leveraging observed correlations and complementarity between them, and specifically through targeted fact extraction from the biomedical literature. These methods will be applied at scale across millions of published articles, to infer and validate functional information.