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bioRxiv · 10.1101/2024.10.29.621006

AutoPM3: Enhancing Variant Interpretation via LLM-driven PM3 Evidence Extraction from Scientific Literature

Li, S.·Wang, Y.·Liu, C.-m.·Huang, Y.·Lam, T.-w.·Luo, R.
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Abstract

Rare diseases, affecting 300 million people globally, often result from genetic variants. Wholegenome sequencing has made variant detection more cost-effective, but interpreting these variants remains challenging. Current clinical practice combines quantitative evidence and literature, which is complex and time-consuming. We introduce AutoPM3, a method for automating the extraction of ACMG/AMP PM3 evidence from scientific literature using open-source LLMs. It combines an optimized RAG system for text comprehension and a TableLLM equipped with Text2SQL for data extraction. We evaluated AutoPM3 using our collected PM3-Bench, a dataset from ClinGen with 1,027 variant-publication pairs. AutoPM3 significantly outperformed other methods in variant hit and in trans variant identification, thanks to the four key modules. Additionally, we wrapped AutoPM3 with a user-friendly interface to enhance its accessibility. This study presents a powerful tool to improve rare disease diagnosis workflows by facilitating PM3-relevant evidence extraction from scientific literature.

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Li, S., Wang, Y., Liu, C.-m., Huang, Y., Lam, T.-w., Luo, R.. 2024-11-03. AutoPM3: Enhancing Variant Interpretation via LLM-driven PM3 Evidence Extraction from Scientific Literature. https://doi.org/10.1101/2024.10.29.621006

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