DAFANet: A Dynamic Adaptive Feature Alignment Network for Enhanced miRNA Biomarker Classification in Cancer Diagnostics

For proper cancer classification, miRNA biomarker signatures are required. Model flaws are addressed by this study. proposes the innovative Dynamic Adaptive Feature Alignment Network (DAFANet) for miRNA biomarker classification accuracy. DAFANet creates a Multi-Scale Feature Alignment Module (MSFAM) to align miRNA datasets' localized and global feature maps. The framework's main theoretical contribution is the Contextual Feature Refinement Mechanism (CFRM), which dynamically adjusts feature weights to match contextual cues for accurate classification. Progressive Hybrid Transfer Learning (PHTL) fine-tunes pre-trained networks at shallow and deep layers to maximize domain-specific feature extraction, improving DAFANet's efficiency. Because miRNA datasets have intrinsic temporal feature inconsistency, the Temporal Feature Consistency Loss (TFCL) is presented to give uniform temporal feature representation despite dataset differences. The model uses a Two- Phase Semi-Supervised Learning Paradigm to generate new pseudo-labels to improve accuracy in cases with minimal biological data. Consistent experimental investigation shows that DAFANet achieves 99.1% classification accuracy, outperforming previous methods. This model has excellent noise immunity, can be scaled for larger datasets, and applies to many cancer biomarker values. By emphasizing hierarchical feature alignment, sophisticated transfer learning, and temporal consistency optimization, DAFANet helps oncologists create better miRNA biomarker classification tools, improving cancer diagnosis and treatment.outcomes.