基于深度学习的自动参考基因组形计与患者人口统计和肌素相关联
Brandon Ginley1, Nicholas Lucarelli2, Jarcy Zee3
1Departments of Pathology & Anatomical Sciences, University at Buffalo Jacobs School of Medicine and Biomedical Sciences - The State University of New York, Buffalo, NY, USA.
bioRxiv : the preprint server for biology
|June 9, 2023
概括
这项研究使用深度学习来分析健康的人类脏组织,揭示了与年龄,性别和血清肌素相关的脏结构的显著差异. 自动化分析提供了高效和严格的组织形态测量见解.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算病理学计算病理学
- 医学成像分析 医学成像分析
背景情况:
- 建立健康人体脏的参考组织形态测量数据是具有挑战性的,因为复杂的量化.
- 机器学习可以将组织形态测量特征与临床参数相关联,以了解人口变异.
- 这项研究使用深度学习研究组织形态测量与患者年龄,性别和血清肌素 (SCr) 之间的关系.
结论:
- 深度学习能够自动,精确地测量脏组织形状特征.
- 参考脏组织中的基因形状特征与患者人口统计学和SCr.有显著的相关性.
- 深度学习提高了脏研究的基因组学分析的效率和严格性.
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