通过可学习的全球子集扰乱来理解转录监管冗余性
Junhao Liu1, Siwei Xu1, Dylan Riffle2
1University of California, Irvine.
Proceedings of machine learning research
|December 12, 2025
概括
我们开发了GRIDS,这是一种计算方法,用于识别控制基因表达的 cis-regulatory element (CRE) 集,这些集一起工作以控制基因表达. GRIDS揭示了复杂的调节冗余,进步了我们对细胞调节的理解.
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:
- 通过 cis 调节元件 (CREs) 的转录调节对生物功能至关重要.
- 破坏CREs可能导致疾病,它们的冗余性需要识别协作监管集的方法.
- 了解CREs如何协同工作是解读基因表达控制的关键.
研究的目的:
- 引入GRIDS,一种用于剖析组合式CRE效应的in silico计算方法.
- 识别协作有效调节基因表达的CREs集,并考虑冗余性.
- 为CREs提供基因调节的全球解释.
主要方法:
- GRIDS采用双阶段方法作为一个全球特征解释挑战.
- 它构建了一个可分化的代用函数来建模复杂的基因调节过程,并使交叉模式的翻译成为可能.
- 在状态过渡框架内可学习的干扰被用于组合特征景观的高效导航.
主要成果:
- 与现有方法相比,GRIDS在综合基准中表现出优越的解释能力.
- 来自GRIDS的全球解释揭示了跨不同细胞类型和状态的复杂监管冗余.
- 该方法有效地导航了CREs复杂的组合特征景观.
结论:
- GRIDS提供了一种强大的计算方法,以了解组合的cis-regulatory元素效应.
- 这些发现凸显了监管冗余在蜂监管中的重要性.
- GRIDS有可能显著推进基因表达和疾病机制的生物研究.
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