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WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

机译:WIPER:生物分子关联网络的加权路径内边缘排名

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摘要

Background: In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes. Method: We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models. Results: Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation. Conclusion: We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks. Availability: The free WIPER API is described at the website (discovery.informatics.uab.edu/wiper/).
机译:背景:在网络生物学中,研究人员利用高通量数据和已知的生物分子关联实验性地衍生出候选基因或蛋白质,从而生成生物分子网络。当前的生物信息学研究集中于表征候选基因/蛋白质或节点,具有网络特征,例如中间性。但是,很少有研究报告能够表征和区分生物分子关联(“边缘”)的优先级,这些关联可以代表生物过程必不可少的基因调控事件。方法:我们开发了加权路径内边缘排名(WIPER),这是一种新的计算算法,可以帮助评估网络模型中的所有生物分子相互作用/缔合(“边缘”),并根据路径内的路径生成每个边缘的排名顺序遍历得分和统计显着性检验结果。为了验证WIPER是否按照我们的设计工作,我们在综合网络模型上测试了该算法。结果:我们的结果表明,WIPER可以可靠地发现关键的“遍历路径内边缘”(统计上比正常边缘遍历更多)和“外围路径内边缘”(其遍历比正常边缘少)。与中介中心性等其他简单度量相比,WIPER提供了更好的生物学解释。在分析肛门后猪心脏基因表达的案例研究中,WIPER强调了提示心肌细胞再生和增殖的新信号通路。在阿尔茨海默氏病遗传失调关联的案例研究中,WIPER报告了PubMed共引文在统计和生物学上都具有重要的SRC:APP,AR:APP,APP:FYN和APP:NES边缘(基因-基因关联)。结论:我们相信,WIPER将成为必不可少的软件工具,以帮助生物学家在生物网络的背景下从高通量生物学数据中发现并验证必要的信号/调节事件。可用性:网站(discovery.informatics.uab.edu/wiper/)上描述了免费的WIPER API。

著录项

  • 来源
    《Quantitative biology》 |2019年第4期|313-326|共14页
  • 作者单位

    Informatics Institute School of Medicine University of Alabama Birmingham AL 35233 USA;

    Department of Biomedical Engineering University of Alabama Birmingham AL 35233 USA;

    Informatics Institute School of Medicine University of Alabama Birmingham AL 35233 USA Department of Biomedical Engineering University of Alabama Birmingham AL 35233 USA Department of Computer Science University of Alabama Birmingham AL 35233 USA;

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