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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >A network-based approach to mine temporal genes exhibiting significant expression variation in Caenorhabditis elegans
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A network-based approach to mine temporal genes exhibiting significant expression variation in Caenorhabditis elegans

机译：一种基于网络的方法来挖掘秀丽隐杆线虫中明显的表达变异的时态基因

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It is critical to be able to identify longitudinally changing genes in temporal data so that studies can be focused on how gene expression changes in a dynamic way. While biological networks continue to play a significant role in modeling and characterizing complex relationships in biological systems, most network modeling studies in biomedical research focus on snapshot or “static” network-based analysis to identify genes of interest. In this study, we use a temporal non-sampling network-based approach to identify and rank genes that exhibit significant co-expression variation over time. We use in the C. elegans gene correlation network obtained from mRNA expression profiles to illustrate the value of the proposed approach. We compare the results of this method to results obtained from traditional statistical analysis that focuses on identifying simple differentially expressed genes. We show that rank-based temporal network analysis can identify genes that contribute to changes in the network structure and consequently contribute to changes in the genetic regulatory machine.
机译：能够识别时间数据中纵向变化的基因至关重要，这样研究才能集中于基因表达如何动态变化。尽管生物网络在建模和表征生物系统中的复杂关系方面继续发挥重要作用，但生物医学研究中的大多数网络建模研究都集中在基于快照或“静态”网络的分析中，以识别目标基因。在这项研究中，我们使用基于时间非采样网络的方法来识别和排名随时间推移表现出明显共表达差异的基因。我们在秀丽隐杆线虫基因相关网络中使用从mRNA表达谱获得的数据来说明所提出方法的价值。我们将这种方法的结果与从传统统计分析中获得的结果进行比较，传统统计分析的重点是确定简单的差异表达基因。我们表明，基于等级的时态网络分析可以识别有助于网络结构变化的基因，从而有助于基因调控机器的变化。

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