Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database.
Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database.
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DOI：
10.1371/journal.pone.0046524
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Mattingly CJ
中科院分区：
文献类型：
--
作者：
King BL;Davis AP;Rosenstein MC;Wiegers TC;Mattingly CJ
接触环境中的化学物质被认为在许多人类疾病的病因学中起着关键作用。为了加强对环境对人类健康影响的了解，比较毒素基因组学数据库(CTD；http://ctdbase.org))提供了独特的精选数据，使人们能够开发关于化学物质和疾病之间关系的新假说。CTD生物策略师阅读文献，整理化学物质--基因、基因--疾病和化学--疾病之间的直接关系。然后，这些直接关系被计算集成，以创建额外的推断关系；例如，直接的化学-基因陈述可以与直接的基因-疾病陈述相结合，以生成化学-疾病推断(通过共享的基因推断)。在CTD中，随着直接化学、基因和疾病相互作用的数量增加，推断的数量呈指数级增加。为了帮助用户导航这些推论，并为假设开发确定优先顺序，我们实现了一个统计，根据由化学物质、疾病和用于进行推理的每个基因组成的本地网络的拓扑结构，对这些推论进行评分和排名。在这个网络中，化学物质、疾病和基因是由代表精心策划的相互作用的边连接起来的节点。与其他生物网络一样，节点的连通性是评价CTD网络的一个重要因素，因为节点的连通性服从幂函数分布。拓扑方法减少了生物网络中存在的高度连接的节点的影响。我们评估了已发表的方法，这些方法使用本地网络拓扑来确定来自高通量分析的蛋白质-蛋白质相互作用的可靠性。我们开发了一种新的度量方法，它结合了这两种方法并对其进行加权，并唯一地考虑了公共邻居的数量和涉及的每个实体的连接性。我们提出了几个CTD推论作为案例研究，以证明这一指标的价值和推论的生物学相关性。
Exposure to chemicals in the environment is believed to play a critical role in the etiology of many human diseases. To enhance understanding about environmental effects on human health, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) provides unique curated data that enable development of novel hypotheses about the relationships between chemicals and diseases. CTD biocurators read the literature and curate direct relationships between chemicals-genes, genes-diseases, and chemicals-diseases. These direct relationships are then computationally integrated to create additional inferred relationships; for example, a direct chemical-gene statement can be combined with a direct gene-disease statement to generate a chemical-disease inference (inferred via the shared gene). In CTD, the number of inferences has increased exponentially as the number of direct chemical, gene and disease interactions has grown. To help users navigate and prioritize these inferences for hypothesis development, we implemented a statistic to score and rank them based on the topology of the local network consisting of the chemical, disease and each of the genes used to make an inference. In this network, chemicals, diseases and genes are nodes connected by edges representing the curated interactions. Like other biological networks, node connectivity is an important consideration when evaluating the CTD network, as the connectivity of nodes follows the power-law distribution. Topological methods reduce the influence of highly connected nodes that are present in biological networks. We evaluated published methods that used local network topology to determine the reliability of protein–protein interactions derived from high-throughput assays. We developed a new metric that combines and weights two of these methods and uniquely takes into account the number of common neighbors and the connectivity of each entity involved. We present several CTD inferences as case studies to demonstrate the value of this metric and the biological relevance of the inferences.
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影响因子：
10.1
作者：
Jordan, Nicholas;Warner, Keith Douglass
通讯作者：
Warner, Keith Douglass
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影响因子：
11.4
作者：
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