Browsing by Author "Zemedkun Abebe"

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    BWAF-Net: Enhanced Human Promoter Identification via Biologically Weighted Attention Fusion of Transformer and Graph Attention Networks
    (Addis Ababa University, 2025-10) Zemedkun Abebe; Adane Letta (PhD)
    The identification of gene promoter regions is crucial for understanding transcriptional regulation, yet computational methods often struggle to effectively integrate the diverse biological signals involved. Existing approaches typically focus on a single data modality, such as the DNA sequence, or employ simple fusion techniques that fail to leverage explicit biological knowledge. To address these limitations, we present BWAF-Net, a novel multi-modal deep learning framework for the identification of human promoters. BWAF-Net integrates three data streams: DNA sequences processed by a Transformer to capture long-range dependencies; gene regulatory context from 36 tissue-specific networks modeled by a Graph Attention Network (GAT); and explicit domain knowledge in the form of 11 quantified biological motif counts (priors). The framework’s central innovation is the Biologically Weighted Attention Fusion (BWAF) layer, which uses the biological priors to learn dynamic attention weights that modulate the fusion of the sequence and network representations. Evaluated on a balanced dataset of 40,056 human promoter and non-promoter sequences, BWAF-Net achieved outstanding performance, with 99.87% accuracy, 99.99% AUC-ROC, and 100% precision on the held-out test set. The proposed framework significantly outperformed a replicated state-of-the-art, sequence-only baseline as well as a series of ablated models. Our ablation studies confirm that naive feature concatenation is a suboptimal fusion strategy, validating the necessity of the intelligent BWAF mechanism. By providing a framework that is highly accurate, parameter-efficient, and interpretable, this work presents a significant advance in multi-modal AI for regulatory genomics.