机器学习方法用于基因调节网络推断推断的基因调节网络
Akshata Hegde1,2, Tom Nguyen1,2, Jianlin Cheng1,2
1Department of Electrical Engineering and Computer Science, University of Missouri, 416 S 6th St, Columbia, MO 65201, United States.
Briefings in bioinformatics
|September 18, 2025
概括
这篇评论探讨了用于推断基因调节网络 (GRNs) 的机器学习,并突出了AI.
科学领域:
- 计算生物学和生物信息学
- 基因组学和系统生物学
背景情况:
- 基因调节网络 (GRNs) 控制基因表达以响应生物信号.
- 高通量测序和计算生物学已经推进了GRN推理.
- 人工智能 (AI),特别是机器学习 (ML),对于分析omics数据以了解基因相互作用至关重要.
研究的目的:
- 提供基于ML的GRN推断方法的全面审查.
- 支持GRN推理的应用和新的ML方法的开发.
- 讨论数据集,评估指标和GRN推断中的挑战.
主要方法:
- 对GRN推断的监督,无监督,半监督和对比学习技术的审查.
- 分析常用的数据集和现场评估指标.
- 强调深度学习方法,以提高推断性能.
主要成果:
- 人工智能和机器学习技术显著提高了GRN推断的准确性.
- 深度学习方法在提高GRN推理性能方面表现有前途.
- 确定了用于评估GRN推理模型的常用数据集和指标.
结论:
- 机器学习,特别是深度学习,是破译复杂GRN的强大工具.
- 对新的ML方法和解决当前挑战的进一步研究将推动GRN推断.
- 本综述是GRN推断和ML开发研究人员的指南.
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