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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs.

Shuya Abe1, Shinichiro Tago1, Kazuaki Yokoyama2

  • 1Artificial Intelligence Laboratory, Fujitsu Research, Fujitsu Ltd., Kawasaki 211-8588, Japan.

Cancers
|February 25, 2023
PubMed
Summary

We developed an explainable AI (XAI) using knowledge graphs to accurately identify disease-causing genetic variants. This AI tool enhances genomic medicine by providing clear explanations for variant identification.

Keywords:
cancer genomic medicinedeep learningexplainable AIknowledge graphprecision medicine

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Genomic Medicine
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Identifying genetic variants is crucial for treating genetic diseases.
  • The large number of variants necessitates advanced computational approaches like AI.
  • Current methods may lack accuracy or interpretability in variant identification.

Purpose of the Study:

  • To develop an AI model for accurate identification of disease-causing genetic variants.
  • To enhance the explainability of AI models in genomic medicine.
  • To support physicians in diagnosing genetic conditions.

Main Methods:

  • Developed an explainable AI (XAI) model.
  • Integrated genomic medicine databases to construct a knowledge graph.
  • Utilized the knowledge graph for AI-driven variant identification and explanation.

Main Results:

  • The XAI model demonstrated high estimation performance.
  • The XAI model provided clear explanations for its predictions.
  • Performance was benchmarked against traditional methods like random forests and decision trees.

Conclusions:

  • The proposed XAI, leveraging knowledge graphs, achieves high accuracy and explainability.
  • This approach facilitates the advancement and adoption of genomic medicine.
  • The tool aids in the precise identification of disease-causing variants.