BCTI:一种基于贝叶斯网络的方法,用于揭示复杂生物系统中的关键过渡
Yuyan Tong1, Renhao Hong1, Na Yang1
1School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China.
PeerJ
|February 18, 2026
概括
我们开发了贝叶斯临界转换推理 (BCTI) 来使用基因调节网络检测临界疾病状态. BCTI识别了精准医学的早期预警信号,并揭示了潜在的分子机制.
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:
- 识别疾病进展中的关键状态对于预防和精确治疗至关重要.
- 早期预警信号的传统方法往往忽视因果关系,限制机械洞察力.
- 了解分子调节机制是解读疾病动态的关键.
研究的目的:
- 开发一种新的计算方法,用于检测生物系统中的关键过渡.
- 整合网络拓动态和系统状态评估,以实现强大的早期预警信号检测.
- 提高推动疾病进展的分子调节机制的解释性.
主要方法:
- 贝叶斯临界转换推理 (BCTI) 集成了相互信息和结构方程模型.
- BCTI捕捉了基因调节网络拓学的随时间或疾病阶段的动态变化.
- 网络评分机制对系统状态进行定量评估,以检测关键过渡.
主要成果:
- 在推断基因调节网络方面,BCTI在推断基因调节网络方面表现出比基准方法更高或同等的准确性.
- 该方法有效地检测了模拟和真实生物数据集中的关键状态.
- 从高维表达数据中,BCTI为精密医学和分子调节提供了新的见解.
结论:
- 通过BCTI,可以有效地检测关键转变和动态监管机制.
- 该方法显示出在系统生物学和精密医学领域有很大的应用潜力.
- BCTI有助于探索疾病进展和发展的关键分子驱动因素.
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