Evaluating convolutional neural networks and transformer architectures for image-based prediction of protein localization in eukaryotic cells - Msipa, Sibongiseni Letticia (Department of Integrative Biomedical Sciences (IBMS), 2026)
Background: Accurate prediction of protein subcellular localization is critical for understanding protein function and guiding experimental research. Recent advances in deep learning have enabled high-throughput image-based methods to tackle this problem by leveraging large-scale immunofluorescence microscopy datasets. The aim of this study is to comparatively evaluate convolutional neural network (CNN) architectures and Transformer- based models for the multi-label classification of protein subcellular localization in eukaryotic cells, using large-scale immunofluorescence image datasets. Methods: In this study, we comparatively evaluated convolutional neural network (CNN) architectures (DenseNet121, Xception, and InceptionV3) and transformer-based models (Vision Transformer and Swin Transformer) for multi-label classification of protein localization in eukaryotic cells. Using 12,565 immunofluorescence images from the Human Protein Atlas—representing 15 subcellular compartments—we performed transfer learning by replacing the final layers of pretrained ImageNet models to accommodate multi-label output. All models were trained with iterative stratification to handle class imbalance and evaluated on held-out test images. Results and discussion: Our findings indicate that CNN-based models, particularly DenseNet121 and Xception, achieve the highest overall accuracy and F1-scores, successfully recognizing both abundant and underrepresented classes. In contrast, transformers demonstrated variable performance. While the Swin Transformer surpassed the Vision Transformer, neither consistently matched CNN performance—likely reflecting the data requirements and hyperparameter sensitivity of transformer architectures. Visualization techniques (Grad-CAM in CNNs and attention maps in transformers) confirmed that well- performing models localize salient features to biologically relevant regions, suggesting they learn meaningful morphological cues Conclusion: These results underscore CNNs' suitability for subcellular localization analysis with moderate-scale datasets, while transformers may require more extensive tuning or larger training sets to reach comparable accuracy. Our findings suggest that CNNs, especially DenseNet121 and Xception, exhibit superior performance over transformer models in predicting protein localization. CNN-based models demonstrate higher accuracy and interpretability, positioning them as preferred choices for advancing functional proteomics and computational drug discovery.