Development of a biomarker database toward performing disease classification and finding disease interrelations

Area of Science:

• Biomedical Informatics
• Molecular Biology
• Computational Biology

Background:

• Biomarkers are crucial for disease diagnosis, prognosis, and treatment, with rapidly increasing and scattered data.
• Existing online resources lack a comprehensive, open-source human biomarker database.
• Integrating biomarker data is essential for a holistic understanding of diseases.

Purpose of the Study:

• To develop a comprehensive, freely accessible online human biomarker database.
• To classify diseases using protein and metabolite biomarkers.
• To explore inter-disease relationships for insights into molecular mechanisms.

Main Methods:

• Developed a human biomarker database integrated into the KNApSAcK family.
• Classified diseases into 18 classes based on National Center for Biotechnology Information definitions.
• Applied network clustering (DPClusO) and hierarchical clustering to protein and metabolite biomarker data for disease classification.

Main Results:

• Created a centralized human biomarker database with extensive information on biomarker-disease relationships.
• Successfully classified diseases based on distinct biomarker profiles.
• Identified relationships among disease classes, offering new perspectives on disease classification.

Conclusions:

• The developed human biomarker database provides a valuable resource for researchers.
• Biomarker-based disease classification offers novel insights into molecular mechanisms.
• This work represents an early approach to classifying diseases using biomarkers.