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<p>The evolutionary divergence of freshwater and marine fish reflects their adaptation to distinct ecological environments, with differences evident in their morphological traits, physiological functions, and genomic structures. Traditional molecular methods often fail to uncover the intricate regulatory relationships among genes under environmental stress. This study proposes the weighted attention gene analysis (WAGA) model, a novel approach that integrates natural language processing (NLP) for protein-coding gene feature representation with deep learning and self-attention (SA) mechanisms. WAGA effectively identifies key genes associated with sensory functions, osmoregulation, and growth and development on the basis of attention weights. The experimental results highlight its effectiveness in revealing genes crucial for ecological adaptation and evolution. This approach is essential for elucidating the mechanisms of ecological adaptability and evolutionary processes, while also offering novel insights and tools to support targeted breeding in aquaculture and fish genomics research.</p>

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Identification of key genes for fish adaptation to freshwater and seawater based on attention mechanism

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Songping Qian,
  • Youjie Zhao,
  • Fangrong Liu,
  • Lei Liu,
  • Qingyang Zhou,
  • Shunrong Zhang,
  • Yong Cao

摘要

The evolutionary divergence of freshwater and marine fish reflects their adaptation to distinct ecological environments, with differences evident in their morphological traits, physiological functions, and genomic structures. Traditional molecular methods often fail to uncover the intricate regulatory relationships among genes under environmental stress. This study proposes the weighted attention gene analysis (WAGA) model, a novel approach that integrates natural language processing (NLP) for protein-coding gene feature representation with deep learning and self-attention (SA) mechanisms. WAGA effectively identifies key genes associated with sensory functions, osmoregulation, and growth and development on the basis of attention weights. The experimental results highlight its effectiveness in revealing genes crucial for ecological adaptation and evolution. This approach is essential for elucidating the mechanisms of ecological adaptability and evolutionary processes, while also offering novel insights and tools to support targeted breeding in aquaculture and fish genomics research.