Bioinformatics (Oxford, England) · 2026
OrchAlign: Orchestrated Multimodal Alignment for Gene Expression Prediction.
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Liu Li, Xv Guipeng, Liu Shujie, Gong Yeyun, Lin Chen
Аннотация
MOTIVATION: Gene expression prediction benefits from integrating DNA sequences and epigenomic signals. Existing approaches typically combine these modalities using simple operations such as concatenation or summation, without explicitly modeling fine-grained token-level cross-modal interactions. Establishing precise cross-modal correspondences remains challenging due to (1) inter-modal discrepancies, and (2) intra-modal structural preservation.
RESULTS: To address these challenges, we propose OrchAlign, an orchestrated multimodal alignment framework that establishes fine-grained cross-modal alignment while preserving intra-modal structure. Our approach first disentangles each modality into shared and specific representations. We then perform focused cross-modal alignment on the shared subspace within a key regulatory region, producing aligned representations with token-level correspondences. Moreover, we enforce consistency between relational structures of the aligned shared representations and their corresponding modality-specific counterparts, enhancing intra-modal structural coherence. Finally, the aligned shared representations and modality-specific representations are passed to the BiMamba backbone for further multimodal fusion. Our experimental results show that OrchAlign achieves state-of-the-art performance in gene expression prediction.
AVAILABILITY AND IMPLEMENTATION: All resources are available at https://github.com/XMUDM/OrchAlign.
CONTACT AND SUPPLEMENTARY INFORMATION: Correspondence should be addressed to Chen Lin (chenlin@xmu.edu.cn). Supplementary information is available online.
RESULTS: To address these challenges, we propose OrchAlign, an orchestrated multimodal alignment framework that establishes fine-grained cross-modal alignment while preserving intra-modal structure. Our approach first disentangles each modality into shared and specific representations. We then perform focused cross-modal alignment on the shared subspace within a key regulatory region, producing aligned representations with token-level correspondences. Moreover, we enforce consistency between relational structures of the aligned shared representations and their corresponding modality-specific counterparts, enhancing intra-modal structural coherence. Finally, the aligned shared representations and modality-specific representations are passed to the BiMamba backbone for further multimodal fusion. Our experimental results show that OrchAlign achieves state-of-the-art performance in gene expression prediction.
AVAILABILITY AND IMPLEMENTATION: All resources are available at https://github.com/XMUDM/OrchAlign.
CONTACT AND SUPPLEMENTARY INFORMATION: Correspondence should be addressed to Chen Lin (chenlin@xmu.edu.cn). Supplementary information is available online.