One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN , along with the documentation and tutorials.

中文翻译：

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动态贝叶斯网络学习从时间序列基因表达数据推断稀疏模型


系统生物学的关键挑战之一是从复杂的高维稀疏数据中导出基因调控网络（GRN）。贝叶斯网络（BN）和动态贝叶斯网络（DBN）已被广泛应用于从基因表达数据推断GRN。 GRN 通常是稀疏的，但用于阐明 GRN 的 BN 结构学习的传统方法通常会产生许多虚假（假阳性）边缘。我们提出了两个新的 BN 评分函数，它们是贝叶斯信息准则 (BIC) 评分的扩展，带有额外的惩罚项，并将它们与 DBN 结构搜索方法结合使用，以找到最大化建议分数的图结构。与 BIC 评分相比，我们的 BN 评分函数为推断网络提供了更好的解决方案，具有更少的虚假边缘。所提出的方法在自回归和 DREAM4 基准上进行了广泛的评估。我们发现，相对于 BIC 分数，它们显着提高了学习图的精度。所提出的方法还在三个实时序列基因表达数据集上进行了评估。结果表明，我们的算法能够从高维时间序列数据中学习稀疏图。这些算法的实现是开源的，并且可以在 GitHub 上以 R 包的形式提供（网址为 https://github.com/HamdaBinteAjmal/DBN4GRN），以及文档和教程。