Leadman Devon, Qu Sue, Xu Yanji, Zhu Qian
Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD.
Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022 Dec;2022:3263-3266. doi: 10.1109/bibm55620.2022.9995172. Epub 2023 Jan 2.
Given the nature of rare diseases, lack of data and standards impedes research in rare diseases. A method to improve data interoperability is necessary to allow data reuse, integration, and exchange in rare disease. A computational package named NormMap was developed to identify rare disease related data from various types of resources in free text via semantic annotation with rare disease terms from NCATS Genetic and Rare Diseases (GARD). In this preliminary study, four different sources which include NIH funded projects, clinical trials, PubMed articles, and Reddit subreddits, were applied to generate rare disease profiles by extending and exploring NormMap. Those profiles would offer a complete view of rare diseases from different aspects, funding agencies, patient groups, scientific research, to ultimately advance rare disease research, which is demonstrated in our case study.
鉴于罕见病的特性,数据的匮乏和标准的缺失阻碍了罕见病研究。需要一种提高数据互操作性的方法,以实现罕见病数据的重复使用、整合和交换。开发了一个名为NormMap的计算包,通过使用美国国立转化医学科学研究所遗传与罕见病(GARD)的罕见病术语进行语义标注,从各种类型资源的自由文本中识别与罕见病相关的数据。在这项初步研究中,应用了包括美国国立卫生研究院资助项目、临床试验、PubMed文章和Reddit子版块在内的四个不同来源,通过扩展和探索NormMap来生成罕见病概况。这些概况将从不同方面,如资助机构、患者群体、科学研究等,提供对罕见病的全面视角,以最终推动罕见病研究,这在我们的案例研究中得到了证明。