Event Description
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
Uncertainty-Aware Adaptation of LLMs for Protein-Protein Interaction Analysis
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.
Biography: Sanket is a research staff member in the Applied Mathematics department within the Computing and Data Sciences Directorate at Brookhaven National Laboratory. Previously, he was the Amalie Emmy Noether Postdoctoral Fellow in the same department. He earned his Ph.D. in Statistics from Michigan State University.. Sanket's research interests span Bayesian statistics, uncertainty quantification (UQ), deep learning, Markov Chain Monte Carlo (MCMC), variational inference, and sparsity methods. He also focuses on dimensionality reduction, surrogate modeling, hybrid physical-data driven models, and active learning, with applications across climate science, materials science, and life sciences.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605691898?pwd=xC7GebG7Kvzxa4AjPSIxJw7e9IZtoY.1
Meeting ID: 160 569 1898
Passcode: 303888
