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A conceptual framework for human-AI collaborative genome annotation.

Xiaomei Li1, Alex Whan2, Meredith McNeil3

  • 1Agriculture and Food, CSIRO, 26 Pembroke Road, Marsfield, NSW 2122, Australia.

Briefings in Bioinformatics
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

Genome annotation accuracy improves with a new Human-AI Collaborative Genome Annotation (HAICoGA) framework. This approach integrates human expertise with artificial intelligence (AI) to accelerate gene structure and function prediction.

Keywords:
artificial intelligencecollaborationconceptual frameworkgenome annotationhumanlarge language model

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Automated genome annotation tools face challenges in accurately predicting gene structures and functions.
  • Manual curation by experts is vital for validating and refining automated predictions.
  • Integrating human expertise with AI is crucial for improving genome annotation accuracy and efficiency.

Purpose of the Study:

  • To propose a conceptual framework, Human-AI Collaborative Genome Annotation (HAICoGA), for enhancing genome annotation.
  • To leverage the synergistic partnership between humans and AI to accelerate the annotation process.
  • To explore the integration of large language models within the HAICoGA framework.

Main Methods:

  • Development of a conceptual framework named HAICoGA.
  • Integration of human expertise with AI capabilities.
  • Exploration of large language model integration for specific annotation tasks.

Main Results:

  • The HAICoGA framework aims to enhance human capabilities in genome annotation.
  • The proposed framework is designed to accelerate the genome annotation process.
  • Integration of AI and human expertise is expected to improve accuracy and efficiency.

Conclusions:

  • The HAICoGA framework offers a novel approach to address challenges in genome annotation.
  • Collaborative efforts between humans and AI, including LLMs, hold significant potential for advancing genomic research.
  • Further research is needed to explore emerging challenges and refine AI-driven annotation strategies.