AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
This work addresses the problem of accurate protein functional annotation for researchers in computational biology, representing a new paradigm rather than an incremental improvement.
The paper tackled the challenge of protein function prediction by addressing annotation scarcity and category imbalance, proposing AnnoDPO, a multi-modal framework using Direct Preference Optimization, which established a new paradigm for biological knowledge integration in protein representation learning.
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.