网络深度影响从细菌转录组中推断基因组的推断,使用无效的自编码器.
Willow Kion-Crosby1,2, Lars Barquist1,2,3
1Helmholtz Institute for RNA-based Infection Research (HIRI)/Helmholtz Centre for Infection Research (HZI), 97080 Würzburg, Germany.
Bioinformatics advances
|July 19, 2024
概括
使用无效自编码器 (DAE) 的深度学习有助于从表达数据中发现细菌基因组. 网络架构影响推断,揭示了在尿病原性大肠杆菌中人类殖民期间诱导的基因.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 公共可用的细菌基因表达数据集正在增长,需要自动化方法来生成假设.
- 排斥自编码器 (DAE) 是神经网络,用于通过将数据集编码到减少层中,从表达数据中推断协调基因调节.
研究的目的:
- 为深度网络推广DAE并探索网络架构对基因组推理的影响.
- 开发和验证一个基于DAE的管道,用于从大肠杆菌的转录数据中提取基因组.
- 确定在尿病原性大肠杆菌中人类殖民期间独特诱导的基因.
主要方法:
- 开发了一个基于DAE的管道,用于从大肠杆菌的转录数据中提取基因组.
- 通过将推断的基因组与已知的生物途径进行比较来验证该方法.
- 研究了不同的DAE网络深度和宽度对基因组恢复和生物推理的影响.
主要成果:
- 增加DAE网络深度导致了更简洁的基因组定义.
- 网络宽度调整呈现了概括性和生物推理准确性之间的权衡.
- 应用了优化的管道来识别在独立的尿病原性大肠杆菌数据集中在人类殖民期间特别诱导的基因.
结论:
- DAE架构显著影响了从基因表达数据中获得的生物见解.
- 开发的管道有效提取生物相关的基因组,并可以识别特定条件的基因诱导.
- 这种方法在细菌基因调节研究中推进了自动化假设生成.
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