对遗传学和基因组学中的机器学习模型评估指标的审查
Catriona Miller1, Theo Portlock1, Denis M Nyaga1
1The Liggins Institute, The University of Auckland, Auckland, New Zealand.
Frontiers in bioinformatics
|September 25, 2024
概括
机器学习 (ML) 提供了对基因组学的见解,但需要仔细评估. 本综述详细介绍了集群,分类和回归的ML指标,解决了基因组数据分析中的潜在陷.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 机器学习 (ML) 对分析大型,复杂的基因组数据集具有重大潜力,以了解疾病风险和病原性.
- 在基因组学中,对ML模型结果的解释至关重要,需要对评估指标的彻底理解,以避免偏见和夸张的发现.
- 基因组研究越来越依赖于机器学习,使得强大的模型评估对于对健康和福祉的可靠见解至关重要.
研究的目的:
- 为基因组数据分析应用的ML评估指标提供全面的概述.
- 突出各种ML指标用于集群,分类和回归任务的优缺点.
- 在基因组学背景下指导研究人员对ML模型结果的批判性评估和利用.
主要方法:
- 在数据科学中评估机器学习模型的既定和新兴指标的审查.
- 基于ML任务对指标的分类:聚类,分类和回归.
- 分析基因组应用的ML模型评估中的常见陷和最佳实践.
主要成果:
- 详细介绍各种ML指标,包括它们的优缺点,用于不同的分析目标.
- 识别在基因组学ML模型的评估阶段遇到的频繁错误和偏见.
- 实践示例说明如何有效地评估和应用在基因组研究中的ML模型结果.
结论:
- 了解ML评估指标对于准确解释基因组发现和减轻潜在危害至关重要.
- 这次审查为研究人员提供了选择适当指标的知识,并避免了基因组学ML模型评估中的常见陷.
- 适当评估ML模型对于充分利用它们在推进遗传和基因组研究方面的全部潜力至关重要.
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