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Related Experiment Video

Updated: Sep 19, 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

Published on: June 13, 2025

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Associative knowledge graphs for efficient sequence storage and retrieval.

Przemysław Stokłosa1, Janusz A Starzyk2, Paweł Raif3

  • 1Institute of Management and Information Technology, Bielsko-Biała, Poland.

Computer Methods and Programs in Biomedicine
|June 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Sequential Structural Associative Knowledge Graphs (SSAKGs) for efficient sequence storage and retrieval. The Weighted Edges Node Ordering algorithm achieves high precision in tasks like anomaly detection and genetic analysis.

Keywords:
Associative knowledge graphsContext-based associationGraph densitySSAKG packageSequence retrievalmiRNA sequences

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Area of Science:

  • Data Science
  • Bioinformatics
  • Computational Neuroscience

Background:

  • Sequence storage and retrieval pose challenges in anomaly detection, behavior prediction, and genetic analysis.
  • Associative Knowledge Graphs (AKGs) offer a solution by encoding sequences using sparse graph structures.
  • Existing methods require improvement in memory capacity and context-based retrieval accuracy.

Purpose of the Study:

  • To develop an efficient method for sequence storage and retrieval using Associative Knowledge Graphs (AKGs).
  • To introduce algorithms for optimizing element ordering within AKGs.
  • To maintain high memory capacity and context-based retrieval accuracy.

Main Methods:

  • Utilized Sequential Structural Associative Knowledge Graphs (SSAKGs) encoding sequences as transitive tournaments.
  • Developed and tested four ordering algorithms: Simple Sort, Node Ordering, Enhanced Node Ordering, and Weighted Edges Node Ordering.
  • Evaluated performance on synthetic and real-world datasets (sentences, miRNA sequences) using precision, sensitivity, and specificity.

Main Results:

  • The Weighted Edges Node Ordering algorithm showed superior precision and graph density resilience.
  • Achieved high precision rates in sentence retrieval (94.7%-97.3%) and miRNA sequence retrieval (99.6%).
  • SSAKGs demonstrated quadratic memory capacity growth relative to graph size.

Conclusions:

  • Introduced a novel structural approach for sequence storage and retrieval with no training requirements.
  • Highlighted flexible context-based reconstruction and high efficiency in sparse memory graphs.
  • The approach offers scalable solutions for sequence-based memory tasks in computational neuroscience and bioinformatics.