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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Effective knowledge management in translational medicine.
Sándor Szalma1, Venkata Koka, Tatiana Khasanova
1Centocor R&D, Inc, 3210 Merryfield Row, San Diego, CA 92130, USA. sszalma@its.jnj.com
Journal of Translational Medicine
|July 21, 2010
Summary
A new knowledge management system integrating human sample data aids drug discovery. This system supports decision-making in translational medicine, accelerating the development of treatments for unmet medical needs.
Area of Science:
- Translational Medicine
- Biomedical Data Science
- Drug Discovery and Development
Background:
- Growing recognition of human samples as a key data source for biomedical discoveries.
- Expansion of translational medicine and science departments in academia and industry.
- Government initiatives like CTSA and FP7 emphasize translating research for human health.
Purpose of the Study:
- To implement an effective knowledge management framework for translational medicine.
- To support drug discovery and development decisions using integrated data.
- To enable rapid hypothesis validation and generation in a translational research setting.
Main Methods:
- Establishment of translational and biomarker departments.
- Implementation of a knowledge management framework with a data warehouse and data mining applications.
- Utilization of open-source systems like i2b2 and GenePattern.
Main Results:
- Deployment across multiple therapeutic areas within Johnson and Johnson.
- Active integration and mining of internal and public data.
- Intuitive graphical interface enabling scientists to quickly re-validate or generate hypotheses.
Conclusions:
- The system provides a foundation for precompetitive data sharing and mining of human subject studies.
- Enhanced understanding of human biology and pathophysiology.
- Facilitation of more effective treatments for unmet medical needs.
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