Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
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.
