A state space representation of VAR models with sparse learning for dynamic gene networks.
A state space representation of VAR models with sparse learning for dynamic gene networks.
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DOI：
10.1142/9781848165786_0006
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发表时间：
2010
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影响因子：
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通讯作者：
Kaname Kojima;R. Yamaguchi;S. Imoto;Mai Yamauchi;Masao Nagasaki;Ryo Yoshida;Teppei Shimamura;Kazuko Ueno;T. Higuchi;N. Gotoh;S. Miyano
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作者：
Kaname Kojima;R. Yamaguchi;S. Imoto;Mai Yamauchi;Masao Nagasaki;Ryo Yoshida;Teppei Shimamura;Kazuko Ueno;T. Higuchi;N. Gotoh;S. Miyano
提出了一种向量自回归模型的状态空间表示及其基于L1正则化的稀疏学习方法，以实现基于时间进程微阵列数据的动态基因网络的有效估计。该方法克服了向量自回归模型和状态空间模型的缺点，克服了向量自回归模型假设时间间隔相等，观测值和系统噪声分离能力差，状态空间模型假设网络结构模块化的缺点。然而，在一个简单的实现中，所提出的模型需要在基于EM算法的参数估计过程中计算大量的逆矩阵。这将所提出的方法的适用性限制在相对小的基因集。因此，我们介绍了一种新的计算技术EM算法，不需要计算逆矩阵。所提出的方法被施加到刺激EGF受体和剂量的抗癌药物，吉非替尼治疗的肺细胞的时间过程的微阵列数据。通过比较估计的网络与使用未处理的肺细胞估计的控制网络，可以发现被抗癌药物扰动的基因，其在估计的网络中的上游和下游基因可能与抗癌药物的副作用有关。
We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.