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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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Introducing the Big Knowledge to Use (BK2U) challenge.

Yehoshua Perl1, James Geller1, Michael Halper2

  • 1Department of Computer Science.

Annals of the New York Academy of Sciences
|October 18, 2016
PubMed
Summary
This summary is machine-generated.

Discovering Big Knowledge (BK) from Big Data presents challenges. This study introduces summarization and visualization methods to make Big Knowledge usable for clinical phenotyping and drug-drug interaction discovery.

Keywords:
Big DataBig Knowledgeclinical phenotypingdrug-drug interactionssummarization of knowledgevisualization of knowledge

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Knowledge Representation

Background:

  • The Big Data to Knowledge (BD2K) initiative aims to extract knowledge from large datasets.
  • A significant challenge arises when the extracted knowledge itself becomes too large to comprehend, termed Big Knowledge (BK).
  • Effective utilization of BK requires high-level mental representations, which are often lacking.

Purpose of the Study:

  • To address the challenge of utilizing Big Knowledge (BK) effectively.
  • To develop and demonstrate summarization and visualization techniques for BK.
  • To explore the application of these techniques in clinical phenotyping and drug-drug interaction discovery.

Main Methods:

  • Distinguished between assertion-based BK and rule-based BK (rule BK).
  • Developed summarization and visualization techniques to capture the overarching structure of BK.
  • Applied these techniques to assertion BK for clinical phenotyping and rule BK for drug-drug interaction discovery.

Main Results:

  • Demonstrated that summarization of intracranial bleeding concepts improves clinical phenotyping compared to traditional methods.
  • Showcased the utility of summarization and visualization for rule BK in discovering drug-drug interactions.
  • Highlighted the potential of these techniques to enhance the creative and proper use of Big Knowledge.

Conclusions:

  • Summarization and visualization are crucial for making Big Knowledge accessible and actionable.
  • These methods offer significant advantages over traditional approaches in specific biomedical applications.
  • Addressing the 'Big Knowledge to Use' challenge is vital for realizing the full potential of Big Data initiatives.