DB-IRES: a deep learning model based on ensemble learning for predicting internal ribosome entry sites

BACKGROUND: Internal ribosome entry sites (IRES) are cap-independent translation initiation elements present in specific viral and cellular mRNAs. They facilitate direct ribosome recruitment for protein synthesis, bypassing the need for the canonical 5' cap structure. Due to their pivotal roles in viral pathogenesis and cellular translational regulation, precise identification of IRES is crucial for advancing mechanistic studies and exploring potential therapeutic interventions. Nonetheless, manual identification is both labor-intensive and costly, while current computational methods exhibit limitations in accuracy and robustness. RESULTS: DB-IRES is a novel deep learning model designed for this purpose, incorporating densely connected 1D convolutional neural network blocks, bi-directional gated recurrent units, and a self-attention mechanism within an ensemble learning framework. Trained with a five-fold cross-validation strategy, the final integrated model exhibits superior and more robust predictive performance compared to existing methods on an independent test set. The model consistently achieves enhanced discriminatory capabilities across multiple evaluation metrics, thereby validating the effectiveness of its hybrid architecture and ensemble design. CONCLUSIONS: DB-IRES serves as a reliable and precise computational tool for predicting IRES elements. Its improved performance enables more in-depth functional investigations of IRES biology and supports wider applications in RNA research and the development of associated therapeutics.