KGBN:使用知识图增强和优化逻辑基因调节网络.
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们开发了KGBN,一种新的计算方法来改进基因调节网络 (GRN) 模型. 这种方法通过知识图表和实验数据来增强GRN,以便在精准医学中更好地预测药物反应.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 逻辑基因调节网络 (GRN) 模型对于理解细胞调节至关重要,但往往仍然不完整和特定于环境.
- 局限性阻碍了它们在药物反应预测和精准医学等领域的应用.
研究的目的:
- 介绍KGBN (知识图增强布尔网络建模),一种用于系统增强逻辑GRN模型的新型计算工作流程.
- 提高GRNs的可解释性,上下文特异性和全面性,用于高级应用.
主要方法:
- KGBN将监管互动从精心策划的知识图表中集成为替代逻辑规则.
- 它保留了现有的GRN模型的验证结构.
- 规则概率是针对实验数据进行数据驱动校准和表示监管不确定性的优化.
主要成果:
- 在急性髓性白血病 (AML) 中应用KGBN证明了它的实用性.
- 扩展现有的GRN以药物向途径和对ex vivo药物反应数据的培训.
- 生成突变特异模型,准确地回顾已知的治疗敏感性和信号依赖性.
结论:
- KGBN提供了一个强大的框架,用于构建可解释,上下文意识和数据驱动的GRN模型.
- 这种方法可以通过更准确的药物反应预测来推进精准医学.
- 工作流程有助于扩展现有模型以实现更广泛的适用性.
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