深度学习用于预测基因调节网络:R中的逐步协议
1Independent Researcher, Hingoli, India. vijaykumar.muley@outlook.de.
Methods in molecular biology (Clifton, N.J.)
|October 6, 2023
概括
这项研究引入了R/RStudio协议,用于基因调节网络重建中的深度学习. 它使得没有编程专业知识的生物学家能够在全基因组范围内预测转录因子-基因相互作用.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 深度学习在复杂的生物问题上表现出色,比如基因调节网络的重建.
- 目前的深度学习工具需要编程技能,限制了生物学家的可访问性.
- 基因调控网络涉及转录因子及其向基因.
研究的目的:
- 为使用R/RStudio的生物学家提供一个可访问的深度学习协议.
- 为了能够对转录因子-基因调节相互作用进行全基因组范围的预测.
- 降低生物研究中应用先进计算方法的障碍.
主要方法:
- 在 R/RStudio.com 中使用了 TensorFlow 和 Keras API.
- 开发了一种用于数据预处理,神经网络设计和培训的协议.
- 采用公开可用的基因表达数据和基因基因表达基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因
主要成果:
- 成功预测了转录因子和基因之间的全基因组调节相互作用.
- 提供了深度学习模型参数调整的见解.
- 证明了该协议对新型监管协会预测的有效性.
结论:
- 该协议使得深度学习可用于预测基因调节网络.
- 研究人员可以通过将深度学习应用于生物数据来获得实践经验.
- 该协议可以适应计算生物学中的各种研究问题.
关键词:
深度学习是一种深度学习.基因调节 基因调节基因调控网络 (GRN) 是一种基因调控网络.在 TensorFlow 系统中使用 TensorFlow.转录因子 转录因子转录性监管网络 转录性的监管网络更多相关视频
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