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P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

机译:P-Finder:从蛋白质-蛋白质相互作用和GO注释重建信号网络

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摘要

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of . Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.
机译:由于大多数复杂的遗传疾病是由细胞信号传导缺陷引起的,因此阐明信号传导级联对于理解其机制至关重要。我们提出了三种新颖的计算算法,以使用全基因组蛋白质-蛋白质相互作用(PPI)网络和基因本体(GO)注释数据重建起始蛋白质和终止蛋白质之间的信号网络。信号网络以多个线性路径的合并形式表示为有向无环图。将高级语义相似性度量标准应用于PPI加权,作为所有这三种方法的预处理。第一种算法根据路径频率向终止蛋白重复扩展节点列表。第二种算法根据网络主题的出现重复添加边缘,这些主题指示链接模式在PPI网络中比在随机图中更频繁地出现。最后一种算法使用信息传播技术,该技术基于路径强度迭代更新边缘方向并合并选定的有向边缘。我们的实验结果表明,在经过充分研究的路径上进行测试时,所提出的算法比以前的方法具有更高的准确性。此外,我们引入了一个称为P-Finder的交互式Web应用程序工具,以可视化重建的信令网络。

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