对齐的深度神经网络用于集成分析,使用高维输入
Shunqin Zhang1, Sanguo Zhang2, Huangdi Yi3
1School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China; Department of Biostatistics, Yale University, New Haven, CT, USA.
Journal of biomedical informatics
|June 30, 2023
概括
本研究介绍了ANNI,这是一种用于整合性分析的新对齐深度神经网络 (DNN) 技术. ANNI有效地借用多个数据集的信息来提高高维的数据分析的性能.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 为像omics这样的高维数据提供了优势.
- 在DNN中,规范化对于变量选择和估计至关重要.
- 整合多个数据集给DNN带来了对齐挑战.
研究的目的:
- 开发一种使用DNN的多个独立数据集的整合分析方法.
- 解决跨DNN借用信息的挑战,以提高性能.
- 通过利用外部信息来增强高维的奥米克数据的分析.
主要方法:
- 开发了ANNI (整合性分析的调整DNN技术).
- 对于正规化的估计,变量选择和信息借款的应用惩罚.
- 为拟议的技术创建了一个有效的计算算法.
主要成果:
- 广泛的模拟显示了ANNI.的竞争性表现.
- 该方法在癌症数据分析中的实用实用性得到了证明.
- 在多数据集DNN分析中,ANNI有效地解决了信息借用方面的挑战.
结论:
- ANNI提供了一个强大的框架,用于高维数据的整合性DNN分析.
- 该技术通过有效地借用跨研究的信息来提高性能.
- ANNI 是一个有价值的工具 omics 数据集成和分析.
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