Zhu Qian, Liu Ruizheng, Vatas Gunjan, Clough Andrew, Xu Yanji, Nguyễn Ðắc-Trung, Mathé Ewy, Sid Eric
Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, Rockville, USA.
Axle Informatics, Inc, Rockville, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2021 Dec;2021:2614-2617. doi: 10.1109/bibm52615.2021.9669645. Epub 2022 Jan 14.
Rare diseases are naturally associated with low prevalence rate, which raises a big challenge due to less data available for supporting preclinical and clinical studies. Therefore, it is critical to fully utilize the accumulated scientific publications in rare diseases over years, in order to access full spectrum of scientific research and enable relevant scientific evidence extraction and generation. In this study, we obtained rare disease related PubMed articles, extracted multiple types of biomedical information, and semantically presented the data in a knowledge graph, which is hosted in Neo4j based on a predefined data model to support further rare disease research.
罕见病自然与低患病率相关，由于可用于支持临床前和临床研究的数据较少，这带来了巨大挑战。因此，充分利用多年来积累的关于罕见病的科学出版物至关重要，以便获取全面的科学研究并实现相关科学证据的提取和生成。在本研究中，我们获取了与罕见病相关的PubMed文章，提取了多种类型的生物医学信息，并在知识图谱中以语义方式呈现数据，该知识图谱基于预定义的数据模型托管在Neo4j中，以支持进一步的罕见病研究。
