最近的进步和机器学习的应用对大米中的蛋白质-蛋白质相互作用预测:挑战和未来的前景
Sarah Bernard Merumba1, Habiba Omar Ahmed1, Dong Fu1
1State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China.
Proteomes
|November 24, 2025
概括
机器学习 (ML) 预测米蛋白与米蛋白相互作用 (PPI),有助于作物改善. 本综述总结了ML方法用于分析米PPI网络,增强抗病能力和耐压能力.
科学领域:
- 植物生物学 植物生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对植物发育,防御和应激反应至关重要.
- 蛋白质形式显著影响PPI动态和米 (Oryza sativa) 的特异性.
- 机器学习 (ML) 为PPI提供了强大的预测和分析能力,补充实验方法.
研究的目的:
- 为在米中预测PPI提供基于ML的方法提供全面的审查.
- 突出ML在米功能基因组学和育种中的应用.
- 确定米PPI研究ML的挑战和未来方向.
主要方法:
- 总结了PPI预测的ML算法最近的进展.
- 讨论与大米PPI相关的特征提取技术和计算资源.
- 审查有关米PPI网络分析中ML应用的现有文献.
主要成果:
- ML模型在预测米PPI方面是有效的,有助于候选基因发现和蛋白质注释.
- 应用包括识别植物病原体相互作用和支持精密育种策略.
- 案例研究表明,ML可以增强大米对各种压力的抵抗力.
结论:
- 基于ML的PPI预测为了解大米生物学和改善作物特征提供了宝贵的见解.
- 解决数据局限性和提高模型通用性是关键的挑战.
- 未来的研究应该探索多学科集成,深度学习和人工智能,用于先进的米PPI网络分析.
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