College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, University of Cape Town, Cape Town, South Africa.
Front Public Health. 2023 Mar 6;11:1125917. doi: 10.3389/fpubh.2023.1125917. eCollection 2023.
COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been 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, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms 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 associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.
在过去的 3 年里，COVID-19 给我们的生活带来了巨大的影响。全球利益相关者提出的各项倡议仍在实施，以应对这一大流行病，并帮助我们吸取未来的教训。虽然疫苗的推出未能遏制所有毒株疾病的传播，但研究界仍在努力开发针对 COVID-19 的有效治疗方法。尽管 Paxlovid 和瑞德西韦已被 FDA 批准用于治疗 COVID-19，但它们并非没有副作用。因此，研究界仍在寻找一种疗效高的治疗方法。为了支持这一努力，在 COVID-19Base 的最新版本（v3）中，我们总结了疫苗推出后在科学文献中强调的与 COVID-19 相关的生物医学实体。使用了八个不同的主题特定词典，即基因、miRNA、lncRNA、PDB 条目、疾病、临床试验注册的替代药物、药物和药物的副作用，来构建这个知识库。我们引入了一个基于 BLSTM 的深度学习模型来预测药物-疾病关联，该模型在预测目的上优于 COVID-19Base 的早期版本中提出的现有模型。这是我们第一次将涵盖与 COVID-19 相关的最大数量的生物医学实体的疾病-基因、疾病-miRNA、疾病-lncRNA 和药物-PDB 关联纳入其中。我们提供了 COVID-19Base 中涵盖的不同生物医学实体的示例和见解，通过将所有这些实体纳入一个单一的平台，为研究界提供基于文献的证据支持。COVID-19Base v3 可从以下网址访问：https://covidbase-v3.vercel.app/。源代码和数据字典的 GitHub 存储库可供社区从以下网址获取：https://github.com/91Abdullah/covidbasev3.0。
