Odibat Omar, Reddy Chandan K
Department of Computer Science, Wayne State University, Detroit, MI 48228, USA.
J Bioinform Comput Biol. 2012 Feb;10(1):1240002. doi: 10.1142/S0219720012400021.
Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differential scale-free networks, and we compared our method with existing methods. The comparisons show that our algorithm outperforms these existing methods. For the real datasets, we apply the proposed algorithm on several gene expression datasets and demonstrate that the proposed method provides biologically interesting results.
识别在两种条件（如正常与癌症）之间改变其表达的基因是一项至关重要的任务，有助于理解疾病的成因。差异网络分析已成为一种强大的方法，用于检测网络结构的变化并识别两个网络之间差异连接的基因。然而，现有的基于差异网络的方法主要依赖于基于基因连通性的成对比较。因此，这些方法无法捕捉网络结构中本质的拓扑变化。在本文中，我们提出了一种新颖的算法DiffRank，它根据基因对两个网络之间差异的贡献对基因进行排名。为实现这一目标，我们定义了两种新颖的结构评分度量：局部结构度量（差异连通性）和全局结构度量（差异介数中心性）。通过在网络结构中传播分数，然后根据这些传播的分数对基因进行排名来优化这些度量。我们在合成数据集和真实数据集上证明了DiffRank的有效性。对于合成数据集，我们开发了一个用于生成合成差异无标度网络的模拟器，并将我们的方法与现有方法进行了比较。比较结果表明，我们的算法优于这些现有方法。对于真实数据集，我们将所提出的算法应用于几个基因表达数据集，并证明所提出的方法提供了具有生物学意义的结果。
