可解释的深度学习框架,以了解阿尔茨海默病患者大脑中的分子变化:对微质激活和性别差异的影响
Maitry Ronakbhai Trivedi1,2, Amogh Manoj Joshi1,2, Jay Shah1,2
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
npj aging
|July 16, 2025
概括
人工智能 (AI) 模型揭示了阿尔茨海默病 (AD) 大脑区域中共享和独特的基因表达模式. 这项研究确定了一种新的与性别相关的基因对 (ZFX/ZFY),与阿尔茨海默氏症女性的神经元损失较大有关.
科学领域:
- 神经科学是一个神经科学.
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 基因表达失调是复杂的,并且在不同大脑区域之间有所不同.
- 了解转录组签名对于识别AD生物标志物和治疗点至关重要.
- 目前的分析方法,如差异基因表达 (DGE),可能无法完全捕捉分子变化.
研究的目的:
- 开发和应用一个深度学习框架来分析AD脑组织中的基因表达.
- 识别AD中不同大脑区域的共同和特定的转录组签名.
- 发现新的分子机制,包括性别特异性差异,是AD病原体的基础.
主要方法:
- 开发了使用AD和控制脑组织 (DLPFC,PCC,HCN) 批量RNA测序数据的多层感知子 (MLP) 深度学习模型.
- 应用无监督的维度转换来推断疾病进展轨迹.
- 利用SHapley添加式扩展 (SHAP) 进行模型解释性,并确定了关键AD涉及的基因用于网络分析.
主要成果:
- 在独立数据集 (MAYO,MSBB) 上,MLP模型展示了强大的分类和预测性能.
- 确定了与AD相关的微质和性别特异的神经元模块中共享的转录基因特征.
- 发现了一种新的与性别相关的转录因子对 (ZFX/ZFY),与女性阿兹海默症患者的神经元损失增加有关.
结论:
- 深度学习为AD分子病理学提供了强大的洞察力,超越了传统的DGE分析.
- 这项研究阐明了微妙的分子变化,并确定了与AD有关的关键细胞通路.
- 确定了与性别相关的基因对,为AD神经退行症中性别二态的新机制性解释提供了新的解释.
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