Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Jan-Mar;8(1):130-42. doi: 10.1109/TCBB.2009.58.
Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.
约束基结构学习算法通常在稀疏图上表现良好。虽然稀疏性并不罕见，但有些领域的基础图可能有一些密集区域；其中一个领域是基因调控网络，这是进行本文所描述研究的主要动机。我们提出了一种新的约束基算法，它既能提高输出质量，又能降低学习基因调控网络结构的计算要求。该算法基于并扩展了 PC 算法。从先验知识中得出两种不同类型的信息；一种是边存在的概率，另一种是与图中其他节点相比，似乎依赖于大量节点的节点。还提出了一种基于基因本体论的基因调控网络验证新方法。我们在合成数据集和真实数据集上证明了所提出算法的适用性和有效性。
