针对不对称基因网络干预的分散增强学习
IEEE transactions on computational biology and bioinformatics
|November 17, 2025
概括
这项研究引入了一种新的深度强化学习框架,用于干预基因调节网络 (GRNs). 该方法通过适应细胞抵抗和不确定性来增强治疗策略,改善疾病治疗结果.
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:
- 基因调节网络 (GRNs) 对细胞功能至关重要,但它们的失调与癌症等疾病有关.
- 对GRNs的干预因细胞适应性抵抗和不完整的基因表达数据而复杂化.
研究的目的:
- 开发一个分散的深度强化学习框架,以有效地干预基因调节网络.
- 为了应对干预期间细胞适应性抵抗和有限的状态信息所带来的挑战.
主要方法:
- 制定GRN干预作为一个不对称的两人零和游戏.
- 推导出对具有完整基因状态知识的细胞的历史依赖性干预政策.
- 使用深度政策梯度方法来接近纳什平衡政策.
主要成果:
- 建议的干预政策显示出强大的表现和高于预期的收益,即使对复杂的细胞适应反应.
- 当系统状态信息完整时,该方法将汇聚到全态纳什平衡.
- 对p53-MDM2和黑色素瘤GRN模型的验证表明在不确定性下具有优越的适应性.
结论:
- 开发的框架为设计基因调节网络的强大干预提供了一个强大的方法.
- 这种方法对改善GRN失调的复杂疾病的治疗策略有显著的前景.
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