AUTHOR=Basit Syed Abdullah , Qureshi Rizwan , Musleh Saleh , Guler Reto , Rahman M. Sohel , Biswas Kabir H. , Alam Tanvir 

TITLE=COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1125917

DOI=10.3389/fpubh.2023.1125917

ISSN=2296-2565

ABSTRACT=COVID-19 has taken a huge toll in our life for the last three years. Global  initiatives from all stakeholders are still in place to  combat this pandemic and learn the lesson from this pandemic to prepare well for future pandemics. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community  still seeks to find an effective therapeutic solution against COVID-19. Although Paxlovid and Remdesivir are approved by FDA against COVID-19, they are not free of side effects. Finding a therapeutic solution with high efficacy is still continuing in the research community. To support this effort, we have summarized the biomedical entities that are linked to COVID-19. In this latest version (v3) of COVID-19Base, we have summarized the biomedical entities that are highlighted in the scientific literature after the vaccine rollout. Eight different topic specific dictionaries i.e.,  gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drug and their side effects were used for building this knowledgebase. We have introduced a BLSTM-based deep learning model to predict the drug-disease  associations outperforming the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB association covering the largest number of biomedical entities related to COVID-19. We have provided examples and insights on different biomedical entities covered in COVID-19Base to support the research community by incorporating all relevant biomedical entities under a single platform to provide evidence based support from literature. COVID-19Base v3 can be accessed from : \url{ https://covid-base.vercel.app/}. The GitHub repository for the source code and data dictionaries are shared for the community in : \url{https://github.com/91Abdullah/covidbasev3.0}.