Survey and Improvement Strategies for Gene Prioritization with Large Language Models

• URL: http://arxiv.org/abs/2501.18794v1
• Date: Thu, 30 Jan 2025 23:03:03 GMT
• Title: Survey and Improvement Strategies for Gene Prioritization with Large Language Models
• Authors: Matthew Neeley, Guantong Qi, Guanchu Wang, Ruixiang Tang, Dongxue Mao, Chaozhong Liu, Sasidhar Pasupuleti, Bo Yuan, Fan Xia, Pengfei Liu, Zhandong Liu, Xia Hu,
• Abstract summary: Large language models (LLMs) have performed well in medical exams, but their effectiveness in diagnosing rare genetic diseases has not been assessed. We used multi-agent and Human Phenotype Ontology (HPO) classification to categorized patients based on phenotypes and solvability levels. At baseline, GPT-4 outperformed other LLMs, achieving near 30% accuracy in ranking causal genes correctly.
• Score: 61.24568051916653
• License:
• Abstract: Rare diseases are challenging to diagnose due to limited patient data and genetic diversity. Despite advances in variant prioritization, many cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their effectiveness in diagnosing rare genetic diseases has not been assessed. To identify causal genes, we benchmarked various LLMs for gene prioritization. Using multi-agent and Human Phenotype Ontology (HPO) classification, we categorized patients based on phenotypes and solvability levels. As gene set size increased, LLM performance deteriorated, so we used a divide-and-conquer strategy to break the task into smaller subsets. At baseline, GPT-4 outperformed other LLMs, achieving near 30% accuracy in ranking causal genes correctly. The multi-agent and HPO approaches helped distinguish confidently solved cases from challenging ones, highlighting the importance of known gene-phenotype associations and phenotype specificity. We found that cases with specific phenotypes or clear associations were more accurately solved. However, we observed biases toward well-studied genes and input order sensitivity, which hindered gene prioritization. Our divide-and-conquer strategy improved accuracy by overcoming these biases. By utilizing HPO classification, novel multi-agent techniques, and our LLM strategy, we improved causal gene identification accuracy compared to our baseline evaluation. This approach streamlines rare disease diagnosis, facilitates reanalysis of unsolved cases, and accelerates gene discovery, supporting the development of targeted diagnostics and therapies.

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