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Related Experiment Video

Updated: May 29, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

A standard based approach for biomedical knowledge representation.

Ariel Farkash¹, Hani Neuvirth, Yaara Goldschmidt

¹IBM Haifa Research Lab, Haifa Univ. Mount Carmel Haifa, 31905, Israel.

Studies in Health Technology and Informatics

|September 7, 2011

Summary

This summary is machine-generated.

New health information standards enable data and knowledge interoperability for clinical research and healthcare. This approach unifies genotypic, phenotypic, and clinical data, enhancing disease analysis and patient-specific predictions.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Health Informatics
Genomics
Clinical Research

Background:

Formalized health information standards facilitate interoperability.
Clinical and genomic data studies accumulate knowledge on genotype-phenotype relationships.
Unified data and knowledge representation is crucial for advanced analysis and collaboration.

Purpose of the Study:

To describe a methodology for capturing and integrating clinical and genomic data with knowledge.
To demonstrate the benefits of this approach within the Hypergenes project for Essential Hypertension.

Main Methods:

Utilized health information standards like HL7 CDA and Clinical Genomics.
Aligned data capture with the CEN EHR 13606 specification.
Developed a methodology for unified data and knowledge persistence.

Main Results:

Successfully captured and integrated diverse health data and knowledge.
Demonstrated enhanced data and knowledge interoperability.
Facilitated analysis for a specific disease (Essential Hypertension) and patient-specific predictions.

Conclusions:

Formalized standards and unified representation are key for healthcare interoperability and clinical research.
The Hypergenes methodology offers significant benefits for both clinical research and healthcare scenarios.
This approach supports advanced data analysis, cross-research collaboration, and decision support applications.