A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show...

Area of Science:

• Biomedical research
• Data management
• Bioinformatics

Background:

• Lack of a unified standard for human disease animal model data classification and coding globally.
• Existing disparate systems lead to inefficient and difficult data retrieval methods.
• Need for a standardized approach to facilitate data collection, storage, retrieval, and sharing.

Purpose of the Study:

• To develop a standardized and unified classification and coding system for human disease animal model resource data.
• To establish a foundation for efficient data management in an internet environment.
• To improve data accessibility and interoperability across research institutions.

Main Methods:

• Investigated existing data classification and coding rules and standards.
• Utilized the Replication Methodology of Animal Models for Human Disease.
• Incorporated standards such as WS/T 306-2009, GB/T 32843-2016, and scientific data management measures.

Main Results:

• Developed a comprehensive letter-and-number based data classification and coding table.
• Created a layered classification infrastructure based on disease systems, animal species, and modeling methods.
• Established a one-to-one match between codes and classification results.

Conclusions:

• Enables a data sharing and joint construction mechanism for research institutes.
• Provides technical support for database construction, exemplified by the National Human Disease Animal Model Resource Center.
• Offers a research-oriented retrieval method supporting current and future epidemic research, including COVID-19.