Multi-Source Data ETL (Extract, Transform, Load) for a Genetic Epilepsy Diagnosis and Treatment Dashboard

Area of Science:

• Genetics
• Bioinformatics
• Epilepsy Research

Background:

• Epilepsy diagnosis and treatment are advancing rapidly due to the identification of numerous associated genes.
• Managing the vast and diverse genetic data presents a significant challenge for researchers and clinicians.
• Efficiently accessing and consolidating this information is crucial for further breakthroughs.

Purpose of the Study:

• To develop a scalable and adaptable database solution for epilepsy genetic data.
• To transform disparate genetic data into a unified and accessible format.
• To facilitate comprehensive data presentation for epilepsy experts.

Main Methods:

• Implementing an Entity-Attribute-Value (EAV) data model.
• Integrating data from various sources and formats.
• Utilizing standard coding systems for data organization.

Main Results:

• A robust database structure capable of handling complex genetic information.
• Standardized data representation for improved interoperability.
• A foundation for a dashboard to present consolidated genetic data.

Conclusions:

• The EAV model provides an efficient method for managing epilepsy genetic data.
• This approach enhances data accessibility and consolidation for experts.
• The developed database supports advancements in epilepsy diagnosis and treatment strategies.