CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer...

Area of Science:

• Bioinformatics
• Public Health
• Oncology

Background:

• The COVID-19 pandemic necessitates integrated data resources for understanding its complex interplay with cancer.
• Existing databases lack centralization and easy accessibility for researchers studying the virus-cancer relationship.
• A unified platform is crucial for disseminating precise information on COVID-19's impact on cancer patients.

Purpose of the Study:

• To develop the COVID-19 Pandemic Database (CO-19 PDB 2.0) as a centralized, accessible resource for COVID-19 and cancer research.
• To integrate diverse datasets including chemical structures, genomic data, images, and literature.
• To provide automated global notifications and data visualization tools for researchers.

Main Methods:

• Systematic collection and organization of 120 datasets from December 2019 to June 2024.
• Categorization of data into six functional groups: chemical structure, digital image, visualization tool, genomic, social science, and literature.
• Implementation using PHP, HTML, CSS, and MySQL, featuring search and auto-notification functionalities.

Main Results:

• The CO-19 PDB 2.0 offers a comprehensive, single-click access to integrated COVID-19 and cancer data.
• Includes functionalities for image analysis, gene sequence retrieval, data visualization, and environmental event tracking.
• Provides global statistics, country-wise distributions, and insights into cancer prevalence, such as top diagnosed cancers in the USA in 2022.

Conclusions:

• CO-19 PDB 2.0 serves as an indispensable, up-to-date resource for researchers, healthcare professionals, and the public.
• Facilitates a deeper understanding of the COVID-19-cancer nexus through accessible, organized data.
• Ensures data integrity by actively managing and updating links, enhancing its value as a reliable information source.