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

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Pubblicato in:BMC Genomics vol. 26 (2025), p. 1-17
Autore principale: Qian, Songping
Altri autori: Zhao, Youjie, Liu, Fangrong, Liu, Lei, Zhou, Qingyang, Zhang, Shunrong, Cao, Yong
Pubblicazione:
Springer Nature B.V.
Soggetti:
FishBase
Chemical analysis
Physiology
Freshwater fish
Accuracy
Seawater
Deep learning
Bioinformatics
Marine fish
Genes
Data mining
Adaptation
Water analysis
Genomes
Osmoregulation
Genomics
Habitats
Fish
Evolutionary genetics
Statistical analysis
Proteins
Gene expression
Artificial intelligence
Sensitivity analysis
Evolution & development
Environmental stress
Ecological adaptation
Aquaculture
Algorithms
Natural language processing
Environmental
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Abstract: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.
ISSN:1471-2164
DOI:10.1186/s12864-025-12089-5
Fonte:Health & Medical Collection