萨克西毒素研究中的人工智能 (AI):了解海洋恐龙体毒素生物合成和演变的下一个前沿
Buhari Lawan Muhammad1,2, Han-Sol Kim1,2, Ibrahim Aliyu3
1Department of Life Science, Sangmyung University, Seoul 03016, Republic of Korea.
Toxins
|January 27, 2026
概括
恐龙鞭状体中萨克西托克素 (STX) 生物合成是复杂的,人们对其了解甚少. 本综述提出了一个人工智能驱动的多omics框架,以解决STX基因识别,调节和预测有害藻类繁殖管理的知识差距.
科学领域:
- 海洋生物学 海洋生物学
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 萨西毒素 (STX) 是一种由蓝藻细菌和恐龙鞭状体产生的强有力的海洋神经毒素.
- 了解dinoflagellates中的STX生物合成对于管理有害藻类繁殖 (HABs) 和性贝类中毒 (PSP) 至关重要.
- 目前存在关于STX基因起源,调节和恐龙鞭状动物生态驱动因素的知识差距.
研究的目的:
- 审查当前对恐龙鞭状动物中STX生物合成的理解.
- 确定STX研究中持续存在的知识差距.
- 提出一个AI集成的多学科框架,以推进STX研究和应用.
主要方法:
- 在STX研究中对基于omics的方法和AI应用的文献综述.
- 关于STX研究的AI集成多学科框架的建议.
- 强调特定的AI模型用于基因识别,进化重建,分子调节和毒素预测.
主要成果:
- 恐龙状STX生物合成基因是零碎的,表达不一致,分布不均.
- 环境因素与STX生产存在不一致的关系,这表明复杂的多层次监管.
- 人工智能为STX研究中的模式识别,分子注释和数据驱动预测提供了强大的工具.
结论:
- 一个人工智能集成的多omics框架可以显著提升STX生物合成在dinoflagellates的理解.
- 这一框架可以改善HAB监测,海鲜安全和PSP风险管理.
- 人工智能驱动的方法对于开发STX相关风险的准确预测模型至关重要.
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