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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
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AI-CURA, an automated LLM workflow for high-accuracy genetic variant classification.

Wei Ma1, Grace Fong1, Joe Lai1

  • 1Hong Kong Genome Institute, 999077 Hong Kong Special Administrative Region, China.

Science Translational Medicine
|June 24, 2026
PubMed
Summary

AI-CURA, a novel framework, uses large language models (LLMs) to automate genetic variant classification for rare disease diagnosis. DeepSeek-R1 demonstrated high accuracy in interpreting evidence and concordance with expert curators.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Large language models (LLMs) show promise in medical applications but are underexplored for rare disease diagnosis in clinical genetics.
  • Advancements in LLM reasoning and transparency can enhance clinical workflows.

Purpose of the Study:

  • To develop and evaluate AI-CURA, a framework for automated genetic variant classification using LLMs.
  • To assess LLM performance in interpreting literature-based evidence for variant classification according to established guidelines.

Main Methods:

  • AI-CURA integrates automated non-literature evidence assessment with LLM-supported literature evidence review.
  • Two LLMs, DeepSeek-R1 and o3-mini-high, were tested for summarizing literature evidence.
  • Prompt engineering and ACMG-rule-specific knowledge bases were utilized for DeepSeek-R1 optimization.

Main Results:

  • DeepSeek-R1 outperformed o3-mini-high, achieving high sensitivity and 100% specificity in interpreting literature-based ACMG rules.
  • AI-CURA demonstrated high concordance with human experts in classifying 150 variants.
  • The framework successfully reanalyzed 150 ClinVar variants with conflicting interpretations.

Conclusions:

  • AI-CURA offers a robust LLM-based framework for automated genetic variant classification in rare disease diagnosis.
  • The study highlights the potential of LLMs, particularly DeepSeek-R1, to improve efficiency and accuracy in clinical genetics workflows.
  • AI-CURA facilitates automated variant reanalysis, addressing challenges with conflicting interpretations.