在生物信息学中的分布式学习:进步和挑战
Yu Shi1, Wei Xu1,2, Pingzhao Hu1,3,4,5,6
1Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada.
Briefings in bioinformatics
|June 27, 2025
概括
分布之外的学习 (OOD) 通过提高各种基因组数据的可靠性来增强生物信息学中的机器学习模型. 本综述探讨了OOD应用,检测和基础模型,以更好地诊断疾病和药物发现.
科学领域:
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
- 基因组数据分析 基因组数据分析
背景情况:
- 生物信息学中的机器学习模型对于基因组数据的解释至关重要.
- 传统模型与分布外 (OOD) 样本进行斗争,导致性能问题.
- 通过改进模型概括,OOD学习解决了这些局限性.
研究的目的:
- 审查生物信息学OOD学习的进展.
- 突出OOD学习在提高模型可靠性和概括性方面的作用.
- 在各种生物信息学子学科中探索OOD应用.
主要方法:
- 关于近期OOD学习进步的全面概述.
- 审查OOD检测技术和基础模型集成.
- 分析模型架构和适应分配转移的方法.
主要成果:
- OOD学习显著提高了对各种基因组数据集的模型性能.
- 最近的技术增强了OOD检测和基础模型集成.
- 各种生物信息学领域都受益于OOD学习应用程序.
结论:
- 在生物信息学中,OOD学习对于强大的机器学习至关重要.
- 在遇到新数据时,它克服了传统模型的局限性.
- 本综述为研究人员提供了一个关于OOD学习对基因组数据分析和人类健康的影响的资源.
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