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Updated: Jul 30, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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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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From centralized to ad-hoc knowledge base construction for hypotheses generation.

Shaked Launer-Wachs1, Hillel Taub-Tabib2, Jennie Tokarev Madem1

  • 1Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.

Journal of Biomedical Informatics
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

Researchers can build personalized scientific knowledge bases using text-mining for hypothesis generation and literature-based discovery (LBD). This approach simplifies knowledge extraction and verification, empowering small teams with tailored research tools.

Keywords:
Extractive searchHypothesis generationKnowledge baseLiterature-based discoveryRapid exploration

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Area of Science:

  • Biomedical Informatics
  • Scientific Knowledge Management

Background:

  • Scientific research generates vast amounts of literature, making it challenging for individual researchers and small teams to stay updated and extract relevant information.
  • Existing knowledge bases often require specialized expertise and significant resources to create and maintain, limiting their accessibility.

Purpose of the Study:

  • To develop and demonstrate a lightweight, ad-hoc knowledge base creation approach for specialized scientific interests.
  • To enable researchers to leverage text-mining for hypothesis generation and literature-based discovery (LBD).

Main Methods:

  • Utilized an extractive search framework for creating knowledge bases with minimal training requirements.
  • Implemented a personalized approach allowing for a higher tolerance of noise, relying on researcher domain expertise for signal separation.
  • Shifted fact verification from exhaustive checks to post-hoc verification of specific entries of interest.

Main Results:

  • Successfully constructed multiple knowledge bases, including those for internal hypothesis generation (e.g., Drug Delivery to Ovarian Tumors) and a public resource (Cell Specific Drug Delivery).
  • Demonstrated the design, construction, and visualization processes for data exploration and hypothesis generation.
  • Validated the effectiveness of constructed knowledge bases through meta-analysis, human evaluation, and in vitro experiments for specific topics.

Conclusions:

  • The developed approach empowers researchers to create personalized, lightweight knowledge bases for specialized scientific needs.
  • This facilitates hypothesis generation and literature-based discovery (LBD) by streamlining knowledge extraction and verification.
  • A web-based platform is available, offering a valuable tool for the rapid construction of tailored scientific knowledge bases.