COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19
This provides a centralized tool for the biomedical research community to explore potential therapeutic treatments for COVID-19, though it is incremental as it builds on existing literature mining techniques.
The authors tackled the problem of organizing biomedical information related to COVID-19 by developing COVID-19Base, a knowledgebase that integrates seven types of biomedical entities from literature mining, resulting in the first dedicated resource of its kind for COVID-19 research.
We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining. To develop COVID-19Base, we mine the information from publicly available scientific literature and related public resources. We considered seven topic-specific dictionaries, including human genes, human miRNAs, human lncRNAs, diseases, Protein Databank, drugs, and drug side effects, are integrated to mine all scientific evidence related to COVID-19. We have employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. To the best of our knowledge, this is the first knowledgebase dedicated to COVID-19, which integrates such large variety of related biomedical entities through literature mining. Proper investigation of the mined biomedical entities along with the identified interactions among those, reported in COVID-19Base, would help the research community to discover possible ways for the therapeutic treatment of COVID-19.