Continuous development of the semantic search preVIEW: from COVID-19 long COVID

被引:7
作者
Langnickel, Lisa [1 ,2 ]
Darms, Johannes [1 ]
Heldt, Katharina [3 ]
Ducks, Denise [3 ]
Fluck, Juliane [1 ,4 ]
机构
[1] ZB MED Informat Ctr Life Sci, Gleueler Str 60, D-50931 Cologne, Germany
[2] Univ Bielefeld, Fac Technol, Grad Sch DILS, Bielefeld Inst Bioinformat Infrastruct BIBI, Bielefeld, Germany
[3] Robert Koch Inst, Burgstr 37, D-38855 Wernigerode, Germany
[4] Univ Bonn, Katzenburgweg 1a, D-53115 Bonn, Germany
来源
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION | 2022年 / 2022卷
关键词
D O I
10.1093/database/baac048
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has demanded dynamic development. Whereas new semantic concepts have been added over time-such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest-the service has been also extended with several features improving the usability and user friendliness. Most importantly, the user is now able to give feedback on detected semantic concepts, i.e. a user can mark annotations as true positives or false positives. In addition, we expanded our methods to construct search queries. The presented version of preVIEW also includes links to the peer-reviewed journal articles, if available. With the described system, we participated in the BioCreative VII interactive textmining track and retrieved promising user-in-the-loop feedback. Additionally, as the occurrence of long-term symptoms after an infection with the virus SARS-CoV-2-called long COVID-is getting more and more attention, we have recently developed and incorporated a long COVID classifier based on state-of-the-art methods and manually curated data by experts. The service is freely accessible under https://preview.zbmed.de
引用
收藏
页数:7
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