双边网络比简单网络更好地代表因果关系:证据,算法和应用程序
Bingran Shen1, Gloria Curozzi2, Dennis Shasha1
1Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, United States.
Frontiers in genetics
|May 27, 2024
概括
基因调节网络往往无法准确预测因果关系. 这项研究表明,从时间序列数据推断出的非线性机器学习模型与黄金标准网络相比,提供了更好的预测性能.
科学领域:
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 基因调节网络 (GRNs) 是基因相互作用的模型.
- GRNs通常使用机器学习推断并根据实验数据验证.
- GRNs的实用性通常是通过它们代表因果关系的能力来评估的.
研究的目的:
- 将从时间序列数据推断出的GRNs的预测性能与黄金标准监管边缘进行比较.
- 评估当前的GRN推断方法是否准确地捕捉了基因调节因果关系.
- 为基因调节中的因果关系研究提出新的目标.
主要方法:
- 从四种物种的时间序列基因表达数据推断出非线性机器学习模型.
- 将这些模型的预测准确性与黄金标准监管边缘进行了比较.
- 计算了根平均平方误差 (RMSE) 的减少,以量化性能改进.
主要成果:
- 从时间序列数据推断出的非线性机器学习模型显示出卓越的预测性能.
- 与基于黄金标准边缘的模型相比,在RMSE减少方面,预测性能的改善范围从5.3%到25.3%.
- 确定目前的GRN推断方法可能无法完全捕捉因果关系.
结论:
- 基因调节网络可能无法准确地描述因果关系.
- 因果关系研究应该优先考虑预测准确性.
- 提出了新的研究方向,包括对预测基因的节制列举,对不连接的预测基因集的识别和双边网络表示.
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