在发育中的神经视网膜中,增强器和沉声器的积极学习
Ryan Z Friedman1, Avinash Ramu1, Sara Lichtarge1
1The Edison Family Center for Genome Sciences & Systems Biology, Saint Louis, MO 63110, USA; Department of Genetics, Saint Louis, MO 63110, USA.
Cell systems
|January 8, 2025
概括
积极学习改善了对cis-regulatory元素的深度学习模型. 这种方法训练模型区分增强器和消声器,即使有相同的序列但有不同的功能.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 对cis-regulatory元素的深度学习模型难以解释上下文依赖的转录因子活动.
- 转录因子,如杆同源盒 (CRX),可以激活或抑制基因表达.
- 区分增强剂和消声剂对于理解基因调节至关重要.
研究的目的:
- 开发一种主动学习方法,用于训练cis-regulatory元素的深度学习模型.
- 为了使模型能够根据转录因子结合部位区分增强剂和消声剂.
- 解决仅在基因组序列上训练的模型的局限性.
主要方法:
- 开发了一个主动学习框架来训练深度学习模型.
- 使用不确定性抽样来选择有信息的培训数据.
- 综合合成生物学和大规模并行报告员测试 (MPRAs) 用于代模型培训.
- 专注于杆同居盒 (CRX) 转录因子的结合部位.
主要成果:
- 训练有素的模型能够区分CRX-bound增强器和沉声器.
- 在具有相同序列但功能相反的监管要素之间成功区分.
- 通过多轮数据生成和培训实现了代模型改进.
- 确立了积极学习作为建模监管DNA的有效策略.
结论:
- 积极学习增强了深度学习模型对cis-regulatory元素的预测能力.
- 这种方法成功地模拟了转录因子的上下文依赖的调节功能.
- 开发的方法提供了一个强大的策略,用于训练具有功能特异性的调节性DNA模型.
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