通过数据和机制驱动的分布式大规模网络模型解码酵母转录调节
Xingcun Fan1,2, Guangming Xiang2, Wenbin Liao1,2
1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, 200237, PR China.
Synthetic and systems biotechnology
|July 18, 2025
概括
我们开发了一种新型神经网络模型 (DLTRNM),用于绘制基因相互作用的地图,并预测转录调节. 该工具简化了复杂的网络,并确定了关键因素,推进了系统生物学研究.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 转录性调节关系控制基因表达和细胞表现型.
- 了解这些复杂的网络对于生物学洞察至关重要.
研究的目的:
- 引入一种新的分布式大规模转录调节神经网络模型 (DLTRNM).
- 整合预先训练的机器学习模型和微调的先前知识.
- 准确预测监管相关性并确定关键转录因子 (TF).
主要方法:
- 开发了DLTRNM,一种新的神经网络模型.
- 将先前的知识整合到预先训练的机器学习模型中.
- 预先训练DLTRNM在泛转录组数据上,并使用Saccharomyces cerevisiae微调时间序列数据.
- 转录因子 (TFs) 和目标基因 (TGs) 之间的定义相互作用.
主要成果:
- DLTRNM准确地预测了监管相关性.
- 该模型简化了复杂的转录监管网络 (TRNs).
- 确定了潜在的关键TF,并补充了现有的子网络.
- 证明了减少计算需求和增强可解释性.
结论:
- DLTRNM是研究转录调节的强大工具.
- 这一进步有助于理解系统生物学中的复杂细胞转录调节.
- 该模型提供了一个更易于解释和计算效率更高的方法.
关键词:
分布式大规模神经网络分布式大规模神经网络机械和数据驱动的.它们中的一种是S. cerevisiae.TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN TRN转移学习转移学习相关概念视频
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