转移学习用于从5'UTR序列中跨背景预测蛋白质表达
Pierre-Aurélien Gilliot1, Thomas E Gorochowski1,2
1School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK.
Nucleic acids research
|June 12, 2024
概括
转移学习有效地适应深度学习模型用于DNA序列设计. 这种方法可以更好地预测大肠杆菌中不同环境中的蛋白质翻译速率,从而使细胞工程更快.
科学领域:
- 合成生物学 合成生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 模型引导的DNA序列设计加速了细胞重编程和复杂生物系统的工程.
- 深度学习模型为基因表达提供了准确的预测,但在跨语境的概括方面存在困难.
- 缺乏模型通用性限制了数据中心方法在不同的遗传和实验环境中的应用.
研究的目的:
- 为了解决DNA序列设计中的深度学习模型的概括限制.
- 展示一种转移学习程序,以适应预训练模型的新环境.
- 为了能够准确地预测来自5'未翻译区域 (5'UTR) 序列的蛋白质翻译速率,在不同的大肠杆菌环境中.
主要方法:
- 使用预训练的深度学习模型进行序列分析.
- 应用了一个简单的转移学习程序来微调模型.
- 在不同的大肠杆菌环境中使用了少量新的测量以进行校准.
- 利用大规模并行报告测试 (MPRA) 来进行特征学习.
主要成果:
- 成功地调整了一个深度学习模型,以在新环境中高精度地从5'UTR序列预测蛋白质翻译速率.
- 证明了学习模型特征的有效转移到不同的遗传和实验环境.
- 展示了少量新测量的实用性,以实现高效的模型校准.
结论:
- 转移学习为DNA序列设计中的深度学习模型的概括提供了有效的解决方案.
- 开发的方法有助于准确预测翻译速率,加速生物工程.
- 发布的模型和校准程序作为未来基于模型的序列设计工作的基础.
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