PubMind is a large language model (LLM)-assisted framework for Publication Mutation and information Discovery, designed to extract variant–disease–pathogenicity relationships directly from biomedical literature.
PubMind is an AI-driven framework that uses large language models (LLMs) to extract genetic variant–disease–pathogenicity associations directly from biomedical literature. It combines fine-tuned BERT models for input filtering with instruction-tuned LLMs for extracting variant, disease, and functional evidence, covering SNVs, CNVs, SVs, and gene fusions. Extracted variants are normalized to genomic and transcript coordinates and stored in PubMind-DB, a web-accessible knowledgebase. Applied to >41M PubMed abstracts and >5M PMC full texts, PubMind-DB contains ~0.7M consolidated unique variants with rich annotations, of which only ~10% overlap with ClinVar—yet >80% of those show concordant pathogenicity labels, including full agreement for four-star expert-reviewed variants. PubMind provides a scalable, generalizable, and open-source framework that transforms unstructured text into structured genomic knowledge, supporting variant interpretation and precision medicine.
Please refer to requirements.txt for required packages.
Please refer to run_PubMind.ipynb for how to use PubMind. All inputs and outputs during this example PubMind run are in the example folder.
PubMind frameworkds includes the following modules:
- Filtering Module (finetuned BERT model)
- Wangwpi/PubMind_finetuned_BERT (Hugging Face)
- Inference Module (instruction-tuned LLM)
- meta-llama/Llama-3.3-70B-Instruct (Hugging Face)
- Normalization Module
- Quality filter (gene name, pathogenicity)
- Variant parser (cDNA, protein, RSID)
- Map to transcript
- Map to genome cooridnates
- MONDO Disease name
- HPO term
PubMind-DB could be accessed here: https://pubmind.wglab.org/
Wang, P. and K. Wang (2025). PubMind: Literature-Based Genetic Variant Extraction and Functional Annotation Using Large Language Models. bioRxiv: 2025.2010.2013.682183.
PubMind is freely available for academic use. For license details, please refer to this page.