ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning.

ABSTRACT: Subcellular localization prediction is crucial for understanding protein functions and cellular processes. Subcellular localization is dependent on tissue and cell lines derived from different cell types. Predicting cell line-specific subcellular localization using the information of protein-protein interactions (PPIs) offers deeper insights into dynamic cellular organization and molecular mechanisms. However, many existing PPI networks contain systematic errors that limit prediction accuracy. In this study, we propose a reinforcement learning approach, ProtLoc-GRPO, to enhance subcellular localization prediction by optimizing the structure of the underlying PPI network. ProtLoc-GRPO learns to rank and retain the most informative PPI edges to maximize the macro-F1 score for cell line-speci

SUBMITTER: Zeng S 

PROVIDER: S-EPMC12330487 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature