机器学习在法医DNA分析中的应用:批判性审查
Mark Barash1, Dennis McNevin2, Vladimir Fedorenko3
1Department of Justice Studies, San José State University, San Jose, CA, United States; Centre for Forensic Science, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia.
Forensic science international. Genetics
|December 12, 2023
概括
机器学习 (ML) 可以通过自动化复杂数据解释来改善法医DNA分析. 本综述介绍了法医科学家的ML能力和应用,弥合了这个新兴领域的知识差距.
科学领域:
- 计算生物学 计算生物学
- 法医科学 法医科学 法医科学
- 遗传学 遗传学 是一个
背景情况:
- 机器学习 (ML) 算法为分析大型数据集提供先进的计算能力.
- 机器学习的应用广泛,现在扩展到法医科学,包括DNA分析.
- 对法医DNA数据的传统手动分析通常是复杂的,耗时的,容易出现错误.
研究的目的:
- 引入机器学习 (ML) 方法及其在法医DNA分析中的潜在应用.
- 在法医DNA分析中批判性地审查有关ML的当前文献.
- 弥合ML专业人员和法医科学界之间的知识差距.
主要方法:
- 审查现有的关于机器学习在法医科学中的应用文献.
- 介绍基本的机器学习概念和算法.
- 讨论法医DNA分析中的具体要求和挑战.
主要成果:
- 机器学习为简化法医DNA数据分析提供了潜力,提高了准确性和可重复性.
- 目前的文献强调了ML在法医DNA应用中的新性和日益增长的兴趣.
- 在ML专家和法医从业者之间存在重大知识差距.
结论:
- 机器学习为推进法医DNA分析技术提供了一个有希望的途径.
- 需要进一步的跨学科合作,以有效地将ML集成到法医工作流程中.
- 解决目前的局限性和知识差距对于在法医科学中成功采用ML至关重要.
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