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Updated: May 15, 2026

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

Chapter 1: Biomedical knowledge integration.

Philip R O Payne¹

¹The Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America. philip.payne@osumc.edu

Plos Computational Biology

|January 10, 2013

Summary

This summary is machine-generated.

Knowledge-based systems are crucial for integrating complex genomic and clinical data in translational bioinformatics. These systems enhance data analysis, reproducibility, and accessibility in biomedical research and healthcare.

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

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Published on: June 13, 2025

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

Area of Science:

Biomedical Informatics
Translational Bioinformatics
Genomic Medicine

Background:

Biomedical research and healthcare are rapidly advancing with new technologies.
Genomic and personalized medicine initiatives necessitate high-throughput data integration.
Translational bioinformatics requires combining large-scale clinical and molecular data.

Purpose of the Study:

To review theoretical frameworks for knowledge-based systems in biomedicine.
To introduce data integration requirements and patterns for translational bioinformatics.
To demonstrate how knowledge-based systems address these needs.

Main Methods:

Review of knowledge-based system theory and core reasoning operations.
Emphasis on applicability within the biomedical domain.
Introduction of data integration patterns for translational bioinformatics.

Main Results:

Knowledge-based systems offer benefits for data integration and analysis.
These systems can improve reproducibility, scalability, and accessibility of complex reasoning.
Applications include clinical decision support and hypothesis discovery.

Conclusions:

Knowledge-based systems are essential for modern translational bioinformatics.
They provide a framework for integrating diverse biomedical data.
Their application enhances the efficiency and impact of research and healthcare delivery.