EXPLORING COVID-19 PATHOGENESIS WITH KNOWLEDGE GRAPHS AND DEEP LEARNING: REVIEW AND PERSPECTIVES

Knowledge graphs (KGs), which represent entities and their relationships in structured semantic networks, have been increasingly applied across a range of diseases, including thyroid disorders, cardiovascular conditions, and neurological disorders.Despite these advancements, current diagnostic methods often face challenges such as incomplete data integration, limited scalability, and reduced diagnostic accuracy.These limitations highlight the need for innovative approaches to address the complex pathogenesis of COVID-19.This review explores the integration of knowledge graphs with deep learning techniques for advancing COVID-19 research and diagnostics.Relevant COVID-19 datasets spanning viral characteristics, transmission patterns, clinical manifestations, and public health outcomes can be transformed into domain-specific knowledge maps that capture essential biomedical entities and their interconnections.By embedding these graphs into low-dimensional continuous vectors, semantic representations can be effectively utilized in deep learning frameworks.Such hybrid models hold promise for improving case prediction, identifying key disease indicators, and enhancing diagnostic accuracy.The fusion of KGs and deep learning not only offers novel insights into the underlying mechanisms of SARS-CoV-2 infection but also provides scalable solutions for real-world applications such as early detection, prognosis, and therapeutic target identification.Ultimately, this approach has the potential to strengthen evidence-based decision-making in pandemic management and contribute to global efforts in mitigating the impact of COVID-19.