The paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs) using multi-resolution features. TRIPLEX captures cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. The model is evaluated on three public Spatial Transcriptomics (ST) datasets and external Visium data from 10X Genomics, demonstrating superior performance in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC) compared to existing state-of-the-art models. The study highlights the importance of integrating multi-resolution features and the effectiveness of the proposed fusion approach in enhancing prediction accuracy.The paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs) using multi-resolution features. TRIPLEX captures cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. The model is evaluated on three public Spatial Transcriptomics (ST) datasets and external Visium data from 10X Genomics, demonstrating superior performance in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC) compared to existing state-of-the-art models. The study highlights the importance of integrating multi-resolution features and the effectiveness of the proposed fusion approach in enhancing prediction accuracy.