Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916899754868736 |
|---|---|
| author | Jantre, Sanket Wang, Tianle Park, Gilchan Chopra, Kriti Jeon, Nicholas Qian, Xiaoning Urban, Nathan M. Yoon, Byung-Jun |
| author_facet | Jantre, Sanket Wang, Tianle Park, Gilchan Chopra, Kriti Jeon, Nicholas Qian, Xiaoning Urban, Nathan M. Yoon, Byung-Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis Jantre, Sanket Wang, Tianle Park, Gilchan Chopra, Kriti Jeon, Nicholas Qian, Xiaoning Urban, Nathan M. Yoon, Byung-Jun Machine Learning Artificial Intelligence Computation and Language Applications 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. |
| title | Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis |
| topic | Machine Learning Artificial Intelligence Computation and Language Applications |
| url | https://arxiv.org/abs/2502.06173 |