基因调节网络推断基于一个非均的动态贝叶斯网络模型,并改进了马尔科夫蒙特卡洛采样
Jiayao Zhang1, Chunling Hu2, Qianqian Zhang1
1College of Artificial Intelligence and Big Data, Hefei University, Hefei, 230031, China.
BMC bioinformatics
|June 24, 2023
概括
这项研究引入了一种新型基因调控网络模型 (FC-DBN),通过使用曼哈顿距离和粒子过来提高准确性和融合,用于非静止基因表达数据分析.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 动态贝叶斯网络 (DBNs) 与非静止基因表达数据进行斗争.
- 现有模型在网络重建准确性和模型融合方面面临挑战.
研究的目的:
- 为非静止基因表达数据增强DBN.
- 提高网络重建准确度和模型融合速度.
主要方法:
- 开发了一种使用曼哈顿距离的MD-birth移动,用于多个变化点过程.
- 实施了基于节点依赖的粒子过的马尔科夫链蒙特卡洛采样方法.
- 提出了最终收动态贝叶斯网络 (FC-DBN) 模型.
主要成果:
- 移动MD-birth增加了多个变化点过程的合理性.
- 取决于节点的粒子过提高了基因调节网络重建的采样效率.
- 与Saccharomyces cerevisiae和RAF数据集的现有模型相比,FC-DBN显示出更高的网络重建准确性和更快的融合.
结论:
- 拟议的FC-DBN模型有效地解决了模拟非静止基因表达数据的局限性.
- 新的方法提高了动态贝叶斯网络的性能,用于基因调节网络推断.
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