用偏好优化提升蛋白质功能注释准确率
AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
- 基于直接偏好优化，对齐生物注释偏好
- 缓解功能类别稀疏与分布不均问题
- 适合蛋白质功能预测研究者使用
解析蛋白质功能仍是蛋白质表示学习中的核心挑战。由于功能注释类别繁多且在生物本体中的标注实例高度不平衡，蛋白质语言模型（PLMs）面临巨大困难。受大语言模型对齐中人类反馈强化学习（RLHF）成功的启发，我们提出AnnoDPO，一种新颖的多模态蛋白质功能预测框架，利用直接偏好优化（DPO）增强注释学习。该方法通过偏好对齐的训练目标，同时应对注释稀缺与类别不平衡双重挑战，为蛋白质表示学习中的生物知识融合建立了新范式。
原文摘要 · Abstract (English)
Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal framework for protein function prediction that leverages Direct Preference Optimization (DPO) to enhance annotation learning. Our methodology addresses the dual challenges of annotation scarcity and category imbalance through preference-aligned training objectives, establishing a new paradigm for biological knowledge integration in protein representation learning.
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