EGA-Ploc: An Efficient Global-Local Attention Model for Multi-Label Protein Subcellular Localization Prediction on...

Protein subcellular localization (PSL) is central to unraveling protein functions and disease mechanisms in bioinformatics. Immunohistochemistry (IHC) images serve as rich sources of high-resolution visual cues for PSL prediction. However, conventional deep learning approaches face critical limitations: whole-image models suffer irreversible fine-grained detail loss during downsampling, while patch-based methods lack effective global context integration. Additionally, the long-tailed class distribution in PSL datasets exacerbates performance degradation for underrepresented classes. To address these challenges, we present EGA-Ploc, a framework employing a linear attention mechanism optimized for high-resolution IHC images. This mechanism enables efficient global and local feature modeling with near-linear computational complexity, facilitating end-to-end processing of original images without resolution loss. Moreover, we propose an adaptive multi-label loss function that integrates zero-bounded log-sum-exp constraints with dynamic class-weighted compensation to mitigate dataset imbalance. Consequently, our EGA-Ploc achieves competitive performance across multiple PSL benchmarks while maintaining computational efficiency superior to existing methods. Through extensive visualization analysis, we further investigate the generalizability of off-the-shelf computer vision models in PSL, uncovering interpretable insights into their subcellular localization mechanisms.