BioGPT: A Generative Transformer-Based Framework for Personalized Genomic Medicine and Rare Disease Diagnosis

Abstract

This paper introduces BioGPT, a generative transformer-based framework designed to advance personalized genomic medicine and rare disease diagnosis. Unlike conventional models that process either genomic sequences or clinical narratives in isolation, BioGPT employs a cross-modal architecture that effectively fuses both data streams, enabling precise classification and interpretable natural language generation. The model is pre-trained on large-scale genomic and electronic health record datasets and fine-tuned for rare disease tasks. Comprehensive experiments demonstrate BioGPT’s superiority over state-of-the-art biomedical models, including RarePT, BioBERT, and DNABERT, with improvements of up to 10% in F1-score and over 20 BLEU points in justification fluency. Ablation studies highlight the essential contribution of cross-attention mechanisms in enhancing multi-modal synergy. Furthermore, attention-based interpretability techniques show strong alignment with expert clinical markers, ensuring trust and transparency in diagnostic outputs. With sub-second inference times and compatibility with edge deployment strategies, BioGPT proves both effective and deployable in real-world clinical settings. This work establishes BioGPT as a robust, scalable, and explainable AI solution, setting a new benchmark for intelligent diagnostic systems in precision medicine.