Multi-kernel feature extraction with dynamic fusion and downsampled residual feature embedding for predicting rice RNA N6-methyladenine sites

Abstract

RNA N$^{6}$-methyladenosine (m$^{6}$A) is a critical epigenetic modification closely related to rice growth, development, and stress response. m$^{6}$A accurate identification, directly related to precision rice breeding and improvement, is fundamental to revealing phenotype regulatory and molecular mechanisms. Faced on rice m$^{6}$A variable-length sequence, to input into the model, the maximum length padding and label encoding usually adapt to obtain the max-length padded sequence for prediction. Although this can retain complete sequence information, resulting in sparse information and invalid padding, reducing feature extraction accuracy. Simultaneously, existing rice-specific m$^{6}$A prediction methods are still at an early stage. To address these issues, we develop a new end-to-end deep learning framework, MFDm$^{6}$ARice, for predicting rice m$^{6}$A sites. In particular, to alleviate sparseness, we construct a multi-kernel feature fusion module to mine essential information in max-length padded sequences by multi-kernel feature extraction function and effectively transfer information through global-local dynamic fusion function. Concurrently, considering the complexity and computational efficiency of high-dimensional features caused by invalid padding, we design a downsampling residual feature embedding module to optimize feature space compression and achieve accurate feature expression and efficient computational performance. Experiments show that MFDm$^{6}$ARice outperforms comparison methods in cross-validation, same- and cross-species independent test sets, demonstrating good robustness and generalization. The application on maize m$^{6}$A indicates the MFDm$^{6}$ARice's scalability. Further investigations have shown that combining different kernel features, focusing on global channel-local spatial, and employing reasonable downsampling and residual connections can improve feature representation and extraction, ensure effective information transfer, and significantly enhance model performance.