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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings.

Murthy V Devarakonda1, Smita Mohanty1, Raja Rao Sunkishala1

  • 1Biomedical Research, Novartis, Cambridge, MA, USA.

Journal of Biomedical Informatics
|April 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using knowledge graph embeddings and inductive inference to recommend clinical trial designs. The approach effectively mines past trial data, improving future clinical trial planning.

Keywords:
Clinical trialsGraph Attention Networks (GATs)Graph Neural Networks (GNNs)Graph embeddingsInductive inferenceKnowledge graphsRecommendation systemsTransductive inference

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Area of Science:

  • Biomedical Informatics
  • Clinical Trial Design
  • Artificial Intelligence

Background:

  • Clinical trial design requires numerous complex decisions.
  • Mining historical clinical trial data can inform these decisions.
  • Existing methods lack comprehensive data-driven recommendations.

Purpose of the Study:

  • To develop a recommendation system for clinical trial design.
  • To leverage knowledge graph embeddings and inductive inference for this purpose.
  • To improve the efficiency and effectiveness of clinical trial planning.

Main Methods:

  • Constructed a novel knowledge graph from clinical trials data.
  • Applied neural embeddings and evaluated various embedding techniques.
  • Utilized a semantics-driven inductive inference method for recommendations.
  • Used publicly available data from clinicaltrials.gov.

Main Results:

  • Achieved relevance scores for recommendations between 70% and 83%.
  • Demonstrated that top-ranked recommendations were highly pertinent.
  • Validated the effectiveness of the proposed knowledge graph and inference method.

Conclusions:

  • Inductive inference with node semantics is effective for generating clinical trial design recommendations.
  • Knowledge graph embeddings offer a powerful approach for mining clinical trial data.
  • Potential exists for further enhancement of graph embedding training using node semantics.