基于概率模糊逻辑的蛋白质信号网络推断方法

Inferring protein-signaling networks by using probabilistic fuzzy logic

提出了一种基于概率模糊逻辑来推断蛋白质信号网络的建模方法,能够同时处理蛋白质信号网络中的随机性和模糊性.采用概率模糊规则建立蛋白质之间的因果关系参数模型,并利用模糊后件分布函数族的差异程度来度量因果关系的强度,进而推断蛋白质之间的因果关系.在模拟数据和人体T细胞实验数据集上的测试结果表明,该方法为蛋白质信号网络建模提供了一种有效的方法.

A novel method of inferring protein-signaling networks via the probabilistic fuzzy logic is proposed. The method can simultaneously model both the stochastic and fuzzy nature of biological networks. The causal relationships among proteins are parameterized by probabilistic fuzzy rules （PFR＇s） ; and then, the strengths of these relationships are measured with the divergences among distribution functions of the then-part fuzzy sets in PFR＇s. According to these strengths, the structure of protein-signaling networks can be determined. The experimental results confirm that the probabilistic fuzzy logical method provides a promising tool for protein-signaling networks inference.