Wu Xiaogang, Pandey Ragini, Chen Jake Yue
School of Informatics, Indiana University, Indianapolis, IN 46202, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6954-7. doi: 10.1109/IEMBS.2009.5333885.
Identifying candidate genes/proteins involved in human disease specific molecular pathways or networks has been a primary focus of biomedical research. Although node ranking and graph clustering methods can help identify localized topological properties in a network, it remains unclear how the results should be interpreted in biological functional context in systems-level. In complex biomolecular interaction networks, biomolecular entities may not have absolute ranks or clear cluster boundary among them. We presented Ant Colony Optimization Reordering (ACOR) method to examine emerging network properties. The task of reordering nodes is represented as the problem of finding optimal density distribution of "ant colony" on all nodes of the network. We applied ACOR method to re-analyze a yeast protein-protein interaction (PPI) network annotated with functional information (i.e., lethality), which revealed intriguing systems-level functional features.
识别参与人类疾病特异性分子途径或网络的候选基因/蛋白质一直是生物医学研究的主要重点。尽管节点排序和图聚类方法有助于识别网络中的局部拓扑特性,但在系统层面的生物学功能背景下,如何解释这些结果仍不明确。在复杂的生物分子相互作用网络中,生物分子实体之间可能没有绝对的排名或清晰的聚类边界。我们提出了蚁群优化重排(ACOR)方法来研究新兴的网络特性。节点重排任务被表示为在网络所有节点上寻找“蚁群”的最优密度分布问题。我们应用ACOR方法重新分析了一个标注有功能信息(即致死性)的酵母蛋白质-蛋白质相互作用(PPI)网络,揭示了有趣的系统层面功能特征。