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分类错位:在大数据生物银行中制造自闭症.
1University of California San Diego, San Diego, CA, USA.
Social studies of science
|October 7, 2024
概括
精神病学研究中的大数据,特别是自闭症谱系障碍 (ASD) 基因组学,面临挑战,因为数据集如何不同地对患者进行分类. 这项研究揭示了重复使用MSSNG和23andMe等数据集如何在自闭症研究结果中产生不一致性.
科学领域:
- 精神病学是一个精神病学.
- 基因组学就是基因组学.
- 大数据科学大数据科学
背景情况:
- 生物学与行为之间的联系是复杂的,挑战目前的精神病学诊断类别.
- 大数据方法已被用于解决这种复杂性,但往往导致进一步的并发症.
- 分类错位,即临床类别在数据集中以不同的方式实例化,是关键问题.
研究的目的:
- 调查大数据重用在自闭症谱系障碍 (ASD) 基因组研究中的作用.
- 检查MSSNG和23andMe等数据集中的不同精神病学分类如何影响自闭症研究.
- 突出在人类基因组学中批判性地评估数据集重复使用的必要性.
主要方法:
- 在自闭症谱系障碍 (ASD) 基因组研究中分析大数据重复使用的混合方法方法.
- 检查常用的数据集 (MSSNG,23andMe) 对于固有的精神病分类差异.
- 分析数据集实例化如何影响研究结果.
主要成果:
- 不同分类方法本质上嵌入广泛使用的基因组数据集 (MSSNG,23andMe).
- 这些数据集的重复使用导致了对自闭症谱系障碍 (ASD) 的科学理解中的不一致性和断层.
- 这种动态使精神病学类别的生物学解释变得复杂.
结论:
- 在人类基因组学中,数据集的重复使用和重组需要高度关注,因为固有的分类差异.
- 了解数据集如何实例化临床类别对于解决精神病学研究中的复杂性至关重要.
- 解决大数据中的分类失调对于推进精神病学及其他领域的基因组研究至关重要.
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