Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.

中文翻译：

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结合基因表达数据和先验知识，利用结构限制通过贝叶斯网络推断基因调控网络

基因调控网络 (GRN) 被认为是提供对细胞系统的清晰洞察和理解的最合适的工具。使用基因表达数据重建 GRN 的最成功的技术之一是贝叶斯网络 (BN)，它已被证明是学习过程中异构数据集成的理想方法。然而，通过使用先验信念或使用网络作为搜索过程的起点，已经实现了先验知识的结合。在这项工作中，考虑了在从基因表达数据中学习 BN 的算法中利用不同类型的结构限制。这些限制将以BN应该满足它们的方式编纂先验知识。所以，这项工作的一个目的是对使用先验知识和基因表达数据从BNs推断GRNs进行详细回顾，但本文的主要目的是研究从表达数据中对BNs的结构学习算法是否能取得更好的效果通过使用结构限制来利用这种先验知识的结果。在实验研究中，表明这种结合先验知识的新方法使我们能够实现更好的逆向工程网络。