Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
This work addresses a key challenge in applying large language models to biomedical research, particularly for researchers and clinicians seeking to understand protein-protein interactions in complex diseases.
This study tackled the challenge of uncertainty in large language models for protein-protein interaction analysis, achieving competitive performance in PPI identification with enhanced reliability. The approach resulted in confidence-calibrated insights into protein behavior, with potential applications in precision medicine and biomedical research.
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature; yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence-calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research.