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中文摘要：
布尔网络是研究基因调控网络的一种非常重要的模型，通过时序数据推理基因之间的调控关系是研究网络动态行为和干预策略的基础。现有的预测研究主要集中在基因之间的调控关系，而对调控基因与目标基因之间的布尔函数的作用方式研究甚少。由于基因调控网络是一种处于有序和无序之间的临界网络，本文研究了众数规则、基于偏斜和基于互信息的三种泛化方法对临界布尔网络的稳态分布距离和灵敏度误差的影响。结果表明合理的泛化能够明显提高预测网络的稳态分布距离和灵敏度误差指标。三种泛化方法中，基于互信息的泛化方法的总体性能最好。
英文摘要：
Boolean network has been a major model to study gene regulatory networks.Lots of work have been focused on inferring networks from time-series data and designing potential intervention policies.However,one important problem still remains unsolved,that is the generalization of Boolean function.In general,the inference algorithms always assume a random Boolean value for the unobserved states.As many theoretical and experimental results support that gene regulatory networks lie between the bound-ary of ordered and disordered regimes,we studied three generalization methods：the majority rule,bias-based and mutual information-based methods.Results both on simulation networks and melanoma network show that reasonable generalization can improve both the steady-state distribution distance and the sensitivity error.And among the three methods,the mutual information-based method performs better than the other two.
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