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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Applications of Large Language Models and Prompt Optimization for Knowledge Extraction from Biological Pathway

Muhammad Azam, Shuai Zeng, Hasanain Aldihis

    IEEE Journal of Biomedical and Health Informatics
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    Summary
    This summary is machine-generated.

    This study integrates Large Language Models (LLMs) and a Genetic Algorithm (GA) to improve gene interaction extraction from biological images. GA-optimized prompts significantly boosted LLM performance in identifying gene relationships.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Artificial Intelligence in Medicine

    Background:

    • Large Language Models (LLMs) show promise in image comprehension.
    • Extracting gene interactions from biological pathway images is crucial for understanding cellular processes.
    • Current methods often rely on traditional Optical Character Recognition (OCR), which can be limited.

    Purpose of the Study:

    • To automate and enhance the extraction of gene interactions from biological pathway images.
    • To evaluate the performance of leading AI chatbots (GPT-4oV, Claude-3.5V, Gemini-1.5V, Llama-3.2V) in this task.
    • To investigate the efficacy of using a Genetic Algorithm (GA) for prompt optimization.

    Main Methods:

    • A dataset of 200 tumor signaling pathway figures was curated from recent literature.
    • Four advanced AI chatbots were assessed for their gene interaction extraction capabilities.
    • A Genetic Algorithm (GA) was employed to optimize prompts for each LLM to improve accuracy.
    • Performance was evaluated on both directional and non-directional gene relationship extraction using F1-scores, precision, and recall.

    Main Results:

    • GA-optimized prompts significantly improved gene interaction extraction accuracy across all tested LLMs.
    • GPT-4oV achieved the highest F1-score for non-directional interactions (0.757) and directional interactions (0.687).
    • Llama-3.2V and Claude-3.5V also showed strong performance, outperforming Gemini-1.5V in most metrics.

    Conclusions:

    • Integrating LLMs with GA-optimized prompts offers a substantial improvement over traditional OCR-based methods for extracting gene interactions from images.
    • While promising, further advancements in LLM accuracy and explainability are necessary for critical biomedical applications.
    • The study provides a benchmark and foundation for developing specialized AI models for biomedical data analysis.