COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19

May 12, 2020 · Declared Dead · 🏛 arXiv.org

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Junaed Younus Khan, Md. Tawkat Islam Khondaker, Iram Tazim Hoque, Hamada Al-Absi, Mohammad Saifur Rahman, Tanvir Alam, M. Sohel Rahman arXiv ID 2005.05954 Category cs.IR: Information Retrieval Cross-listed cs.CL, cs.DL, cs.LG, q-bio.QM Citations 9 Venue arXiv.org Last Checked 4 months ago

Abstract

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.