A central problem of systems biology is the reconstruction of Gene Regulatory Networks (GRNs) by the use of time series data. Although many attempts have been made to design an efficient method for GRN inference, providing a best solution is still a challenging task. Existing noise, low number of samples, and high number of nodes are the main reasons causing poor performance of existing methods. The present study applies the ensemble Kalman filter algorithm to model a GRN from gene time series data. The inference of a GRN is decomposed with p genes into p subproblems. In each subproblem, the ensemble Kalman filter algorithm identifies the weight of interactions for each target gene. With the use of the ensemble Kalman filter, the expression pattern of the target gene is predicted from the expression patterns of all the remaining genes. The proposed method is compared with several well-known approaches. The results of the evaluation indicate that the proposed method improves inference accuracy and demonstrates better regulatory relations with noisy data.

中文翻译：

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KFGRNI：一种基于集成卡尔曼滤波器从时程基因数据中推断基因调控网络的稳健方法

系统生物学的一个核心问题是基因调控网络的重建（GRN) 通过使用时间序列数据。尽管已经进行了许多尝试来设计一种有效的方法GRN推理，提供最佳解决方案仍然是一项具有挑战性的任务。现有噪声、样本数量少和节点数量多是导致现有方法性能不佳的主要原因。本研究应用集成卡尔曼滤波算法对一个GRN来自基因时间序列数据。的推断GRN用 p 个基因分解成 p 个子问题。在每个子问题中，集成卡尔曼滤波器算法识别每个目标基因的相互作用的权重。使用集成卡尔曼滤波器，从所有剩余基因的表达模式中预测目标基因的表达模式。所提出的方法与几种众所周知的方法进行了比较。评估结果表明，所提出的方法提高了推理精度，并展示了与噪声数据更好的监管关系。