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首页> 外文期刊>Interdisciplinary Sciences: Computational Life Sciences >Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks
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Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks

机译：使用具有神经网络的微分方程模型的两步算法重建遗传调控网络

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Background The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. Results We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE~(RNN)/RE~(RMLP)), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE~(RNN)-RNN and RE~(RMLP)-RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. Conclusion The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE~(RMLP)using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE~(RNN)using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE~(RMLP)-RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two-step algorithms can potentially incorporate with different nonlinear differential equation models to reconstruct the GRN.
机译：背景技术遗传调节网络（GRNS）的识别为复杂的细胞过程提供了见解。一类复发性神经网络（RNNS）捕获GRN的动态。提出了组合RNN和机器学习方案的算法来使用基因表达时间序列重建小尺度GRN。结果我们提出了新的GRN重建方法的神经网络。 RNN扩展到一类具有潜节节点的经常性多层的感知（RMLP）。我们的方法包含两个步骤：边缘秩分配步骤和网络施工步骤。前者通过基于RNN / RMLP的电线（RE〜（RNN）/ RE〜（RMLP））的估计重量来分配对所有可能的边缘的排名，并且后者构造由排名级边缘组成的网络优化的RNN模拟基因表达时间序列。应用粒子群优化（PSO）以在两步算法中优化RNN和RMLP的参数。所提出的RE〜（RNN）-RNN和RE〜（RMLP）-RNN算法在约10个基因的综合和实验基因表达时间序列上进行测试。实验时间序列来自酵母细胞周期调节基因和大肠杆菌DNA修复基因的研究。结论使用有限数据点的实验时间序列的RNN不稳定估计可以导致相当任意的预测GRN。我们的方法将RNN和RMLP合并到两步结构学习过程中。结果表明，使用具有合适数量的潜节点的RM〜（rmlp）以减少参数维度的rmlp通常会导致使用在短模拟时间序列上的正则rnn的RE〜（RNN）更精确的边缘等级。由加权多数投票规则组合RE〜（RMLP）-rnn在步骤1中使用不同数量的潜节点来推导的网络，该方法始终如一地执行和优于大多数基准时间序列的GRN重建的发布算法。两步算法的框架可以潜在地包含不同的非线性微分方程模型来重建GN。

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