Global Network Alignment in the Context of Aging
Global Network Alignment in the Context of Aging
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DOI：
10.1109/tcbb.2014.2326862
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发表时间：
2015-01-01
影响因子：
4.5
通讯作者：
Milenkovic, Tijana
中科院分区：
文献类型：
--
作者：
Faisal, Fazle Elahi;Zhao, Han;Milenkovic, Tijana
与序列比对类似，网络比对（NA）可用于在保守网络区域之间跨物种转移生物学知识。 NA 面临两个算法挑战：1）使用哪种成本函数来捕获不同网络中节点之间的“相似性”？ 2）使用哪种比对策略来从所有可能的比对中快速识别“高分”比对？我们“分解”了现有的最先进的方法，这些方法使用不同的成本函数和不同的对齐策略来评估其成本函数和对齐策略的每种组合。我们发现一种方法的成本函数和另一种方法的对齐策略的组合击败了现有方法。因此，我们提出这种组合作为一种新颖的高级 NA 方法。然后，由于人类寿命长，很难通过实验研究人类衰老，因此我们使用 NA 将衰老相关知识从注释良好的模型物种转移到注释不良的人类。通过这样做，我们产生了新的人类衰老相关知识，这补充了目前主要通过序列比对获得的有关衰老的知识。我们证明了我们的新预测的拓扑和功能特性与已知的衰老相关基因的拓扑和功能特性之间的显着相似性。我们是第一个使用 NA 来了解有关衰老的更多信息的人。
Analogous to sequence alignment, network alignment (NA) can be used to transfer biological knowledge across species between conserved network regions. NA faces two algorithmic challenges: 1) Which cost function to use to capture "similarities" between nodes in different networks? 2) Which alignment strategy to use to rapidly identify "high-scoring" alignments from all possible alignments? We "break down" existing state-of-the-art methods that use both different cost functions and different alignment strategies to evaluate each combination of their cost functions and alignment strategies. We find that a combination of the cost function of one method and the alignment strategy of another method beats the existing methods. Hence, we propose this combination as a novel superior NA method. Then, since human aging is hard to study experimentally due to long lifespan, we use NA to transfer aging-related knowledge from well annotated model species to poorly annotated human. By doing so, we produce novel human aging-related knowledge, which complements currently available knowledge about aging that has been obtained mainly by sequence alignment. We demonstrate significant similarity between topological and functional properties of our novel predictions and those of known aging-related genes. We are the first to use NA to learn more about aging.