一个由机制告知的深度神经网络能够优先考虑驱动细胞状态转换的调节器
Xi Xi1, Jiaqi Li1, Jinmeng Jia1
1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China.
Nature communications
|February 3, 2025
概括
我们开发了新型深度神经网络regX,用多omics数据识别驱动细胞状态转换的关键调节者. 这种可解释的AI方法解码了复杂的基因相互作用以获得生物学见解.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 细胞功能由复杂的调节网络在基因和基因相互作用水平上控制.
- 现有的神经网络模型往往缺乏模拟这些调节机制的能力,阻碍了对细胞状态过渡的理解.
- 解读细胞状态转换的调控基础对于理解生物过程和疾病至关重要.
研究的目的:
- 引入 regX,一个深度神经网络,旨在模拟基因水平调节和基因相互作用.
- 为了使负责细胞状态转换的驱动调节器优先.
- 提供对这些监管事件的机制性解释.
主要方法:
- 开发regX,一个集成基因调节和相互作用机制的深度神经网络.
- 将regX应用于2型糖尿病和毛囊发育的单细胞多组数据.
- 转录因子和cis-regulatory元素的优先考虑驱动细胞状态过渡.
主要成果:
- regX成功地确定了关键的转录因子和参与细胞状态转换的 cis 调节元素.
- 分析揭示了潜在的治疗点,药物重定向的机会,以及因果单核酸多态.
- 证明了可解释的神经网络设计能够解码复杂的生物系统的能力.
结论:
- regX提供了一种强大的,可解释的方法来分析单细胞多组数据,以揭示监管机制.
- 这种方法有助于发现新的生物学见解和潜在的治疗策略.
- 突出了可解释AI在促进我们对细胞调节的理解方面的价值.
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