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

SUBMITTER: Liu M 

PROVIDER: S-EPMC11646132 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature