KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.
KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.
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KG-COVID-19：为 COVID-19 响应生成定制知识图的框架。
DOI：
10.1101/2020.08.17.254839
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Mungall,Christopher
中科院分区：
文献类型：
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作者：
Reese,Justin;Unni,Deepak;Callahan,TiffanyJ;Cappelletti,Luca;Ravanmehr,Vida;Carbon,Seth;Fontana,Tommaso;Blau,Hannah;Matentzoglu,Nicolas;Harris,NomiL;Munoz-Torres,MonicaC;Robinson,PeterN;Joachimiak,MarcinP;Mungall,Christopher
关于SARS-CoV-2和COVID-19的综合、最新数据对于生物医学研究界持续应对COVID-19大流行至关重要。虽然SARS-CoV-2和相关病毒（SARS-CoV，MERS-CoV）存在丰富的生物学知识，但整合这些知识是困难和耗时的，因为其中大部分是孤立的数据库或文本格式。此外，研究界所需的数据对于不同的任务有很大的不同;例如，机器学习任务的最佳数据与用于填充临床医生可浏览用户界面的数据大不相同。为了应对这些挑战，我们创建了KG-COVID-19，这是一个灵活的框架，可以摄取和整合异构生物医学数据以生成知识图（KG），并将其应用于创建应对COVID-19的KG。这个KG框架也可以应用于其他问题，其中孤立的生物医学数据必须快速集成用于不同的研究应用，包括未来的流行病。
Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.