对线性和非线性关节嵌入方法的深入比较,用于散装和单细胞多组体的多组体
Stavros Makrodimitris1,2,3, Bram Pronk1, Tamim Abdelaal1,4,5
1Delft Bioinformatics Lab, Delft University of Technology, Street, Postcode, State, Country.
Briefings in bioinformatics
|November 29, 2023
概括
非线性多原子嵌入方法优于线性方法,用于归因缺失的数据并改善疾病结果分析等任务的预测. 产品专家模型在各种应用中表现出强的性能.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 多原子分析对于理解复杂的生物系统和预测疾病结果至关重要.
- 现有的联合嵌入线性方法存在局限性,引发了对非线性神经网络方法的兴趣.
研究的目的:
- 进行线性和非线性关节嵌入方法的对比比较.
- 在批量和单细胞多模式数据集上评估这些方法.
- 为特定下游任务选择合适的联合嵌入技术提供指导.
主要方法:
- 线性和非线性关节嵌入技术的比较.
- 利用了批量和单细胞多模式数据集.
- 对生存分析 (批量数据) 和细胞类型分类 (单细胞数据) 的评估性能.
主要成果:
- 与线性方法相比,非线性方法在归因缺失数据模式方面具有显著的优势.
- 当所有模式都可用时,连接主要组件作为强有力的基线.
- 来自非线性方法的关节嵌入在测试时只有一个模式可用时,提高了性能.
- 来自神经方法的输入式omics配置文件对于以最小的性能损失进行下游分类任务是足够现实的.
结论:
- 非线性联合嵌入方法,特别是专家的产品,为多原子数据集成和归算提供了卓越的性能.
- 嵌入方法的选择影响下游任务性能,非线性方法在缺少数据或有限模式的场景中显示出好处.
- 神经关节嵌入方法促进了现实的数据归算,使生物分析中的可靠预测成为可能.
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