NorLux Neuro-Oncology Laboratory, Centre de Recherche Public de la Santé (CRP-Santé), Luxembourg, Luxembourg.
Biol Direct. 2014 Jun 19;9:12. doi: 10.1186/1745-6150-9-12.
Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.
The method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.
A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.
在生物网络中，节点中心度的度量方法可用于检测具有关键功能作用的基因。在基因共表达网络中，高度连接的基因（即候选枢纽基因）与关键疾病相关途径有关。尽管有不同的方法来估计基因的中心度，但它们在基因共表达网络中的潜在生物学相关性值得进一步研究。此外，基因中心度的标准度量方法侧重于二值交互网络，而在共表达网络的背景下，这可能并不总是合适的。在这里，我还研究了一种基于加权连接分数和统计相关性指标识别潜在生物学意义基因的方法。
该方法能够对网络中基因共表达关联的强度和多样性进行特征描述。它通过在不同的基因共表达网络和生物研究领域中突出更多具有生物学意义的基因，优于标准中心度度量方法。作为该方法基因选择潜力的说明的一部分，我提出了一个在斑马鱼心脏再生中的应用案例。所提出的技术预测了在损伤后组织再生所需的细胞过程中显著涉及的基因。
提供了一种从基因共表达网络中选择具有生物学意义的基因的方法，并提供了免费的开源软件。
