用可解释的AI来建模转录,揭示了促进者和基因体的特定情境表观遗传基因调节
Kashyap Chhatbar1,2, Adrian Bird2, Guido Sanguinetti3
1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
PLoS genetics
|October 23, 2025
概括
深度学习模型使用染色蛋白数据预测RNA聚合酶II (Pol-II) 的占用率. 像SHAP这样的可解释AI (XAI) 方法可以在没有实验的情况下识别调节机制和基因标.
科学领域:
- 分子生物学分子生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 解开染色质相关蛋白在转录调节中的机械作用是复杂的.
- 从蛋白质配置文件中预测RNA聚合酶II (Pol-II) 的占用率在未受到干扰的生物系统中具有挑战性.
研究的目的:
- 开发深度学习模型,从染色质相关蛋白数据中预测Pol-II占用.
- 评估可解释的AI (XAI) 方法,特别是Shapley添加式解释 (SHAP),以推断功能相关性和监管机制.
- 用实验性扰动数据验证人工智能驱动的洞察力.
主要方法:
- 创建深度学习模型来预测Pol-II占用率.
- 应用SHAP (可解释AI) 来分析特征的重要性和监管作用.
- 通过基于degron的扰动实验和交叉数据集分析进行验证.
主要成果:
- 从未受到干扰的数据中SHAP重要性排名成功预测了干扰的直接目标,从而可以在不需要昂贵的实验的情况下推断.
- SHAP分析准确地预测了差异性基因表达和转录变化的程度.
- 在促进体中验证了SET1A和ZC3H4的合作作用,并在基因体中发现了ZC3H4的新型作用.
- 通过H3K4me3和SET1/COMPASS发现了限制器综合体 (ZC3H4) 和整合器综合体 (INTS11) 之间的潜在交叉声.
结论:
- 将预测建模与实验验证相结合,有助于解开复杂的监管网络.
- SHAP分析提供了一个强大的方法,用于在转录调节中生成假设和机械推理.
- 确定了新的调节作用和影响基因表达的潜在复杂相互作用.
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