Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis

• URL: http://arxiv.org/abs/2502.06173v1
• Date: Mon, 10 Feb 2025 05:54:36 GMT
• Title: Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
• Authors: Sanket Jantre, Tianle Wang, Gilchan Park, Kriti Chopra, Nicholas Jeon, Xiaoning Qian, Nathan M. Urban, Byung-Jun Yoon,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions. Yet their inherent uncertainty remains a key challenge for deriving reproducible findings. We present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models.
• Score: 10.67543730905283
• License:
• 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.

Related papers

• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. The model adheres to the central dogma of molecular biology, accurately generating protein-coding sequences. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of promoter sequences.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction [0.4369058206183195]
  Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools for predicting NMIBC recurrence rely on scoring systems that often overestimate risk and have poor accuracy. Machine learning (ML)-based techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data.
  arXiv  Detail & Related papers  (2024-03-15T17:03:45Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks [0.6282171844772422]
  An increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. The recent discovery of named entities as adversarial examples in natural language processing tasks raises questions about their potential impact on the knowledge robustness of pre-trained and finetuned LLMs. We developed an embedding-space attack based on powerscaled distance-weighted sampling to assess the robustness of their biomedical knowledge.
  arXiv  Detail & Related papers  (2024-02-16T09:29:38Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications [0.0]
  This paper proposes a novel computational strategy for the stratification of biomedical problem datasets into k-fold cross-validation (CVs) This approach can improve model stability, establish trust, and provide explanations for outcomes generated by trained IML models.
  arXiv  Detail & Related papers  (2023-03-15T12:02:02Z)
• Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning [10.813777115744362]
  We propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath. The accuracy of the samples with high certainty is almost 100$%$.
  arXiv  Detail & Related papers  (2023-03-11T07:57:37Z)
• Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data [0.8029049649310213]
  We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG) fiBAG allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types.
  arXiv  Detail & Related papers  (2022-12-29T03:31:45Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.