关于蛋白质与蛋白质相互作用的深度学习的最新进展:一篇综述
Jiafu Cui1,2, Siqi Yang1,2, Litai Yi2
1Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China.
BioData mining
|June 16, 2025
概括
深度学习显著推进了计算生物学中的蛋白质-蛋白质相互作用 (PPI) 预测. 本综述涵盖了2021-2025年深度学习方法,解决了PPI分析的挑战和未来方向.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物功能至关重要.
- 深度学习 (DL) 正在彻底改变计算生物学和PPI研究.
- 需要对PPI预测中的最近DL进展进行全面审查.
研究的目的:
- 评估从2021年到2025年的蛋白质-蛋白质相互作用 (PPI) 预测中的深度学习进展.
- 评估核心DL架构和PPI分析中的开创性方法.
- 确定该领域的挑战和未来前景.
主要方法:
- 对包括GNN,CNN和RNN在内的深度学习架构的审查.
- 评估先进的方法:变压器,多任务学习,多式联通和转移学习 (BERT,ESM).
- 对数据不平衡,变异和特征稀疏性的算法进行分析.
主要成果:
- 对PPI预测 (2021-2025) 深度学习模型的详细评估.
- 检查解决数据挑战和行业特定问题的方法.
- 识别新兴趋势和未来的研究途径.
结论:
- 深度学习在PPI预测方面取得了重大进展,增强了分析方法.
- 应对数据挑战和探索新型应用是未来发展的关键.
- 本综述为计算生物学和深度学习研究人员提供了有价值的参考资料.
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