基于深度学习的方法,用于多主题数据集成和分析.
Jenna L Ballard1, Zexuan Wang2, Wenrui Li3
1Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA. jenna.ballard@pennmedicine.upenn.edu.
BioData mining
|October 2, 2024
概括
深度学习通过将生成和非生成方法等方法分类来推进多学科集成. 这些技术增强了数据分析,处理缺失的数据,并整合不同的模式,以获得更好的生物医学见解.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 医疗保健中的人工智能
- 计算生物学是一种计算生物学.
背景情况:
- 深度学习和大数据使复杂,异构的数据融合和分析成为可能.
- 多omics数据 (基因组学,成像) 为整体理解提供了互补的见解.
- 整合不同的数据模式可以显著改善预测和分类任务.
研究的目的:
- 审查和分类基于深度学习的多学科整合方法.
- 讨论该领域的能力和新兴主题.
- 为了解不同深度学习架构的OMIC数据提供一个框架.
主要方法:
- 根据架构 (非生成和生成) 对深度学习方法的分类.
- 分析每个类别的独特优势和弱点.
- 讨论多主题数据集成的新兴趋势.
主要成果:
- 深度学习方法被广泛分为非生成 (feedforward,GCN,自动编码器) 和生成 (变量,GAN,预训练模型).
- 生成性方法可以强制执行约束,结合先前的知识,并归咎于缺失的数据模式.
- 最近的进展使得处理不完整的数据和整合非传统的OMIC数据,包括成像.
结论:
- 预计数据缺失解决方法的增长,这是一个常见的挑战.
- 预计将更多多样化的数据类型集成到下游可以提高任务性能.
- 通过多模式整合对样本的全面观察将推动未来的生物医学发现.
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