Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions.

Francisco Azuaje, Haiying Wang, Huiru Zheng, Olivier Bodenreider, Alban Chesneau
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引用次数: 27

Abstract

There is a need to develop methods to automatically incorporate prior knowledge to support the prediction and validation of novel functional associations. One such important source is represented by the Gene Ontology (GO) and the many model organism databases of gene products annotated to the GO. We investigated quantitative relationships between the GO-driven similarity of genes and their functional interactions by analyzing different types of associations in Saccharomyces cerevisiae and Caenorhabditis elegans. Interacting genes exhibited significantly higher levels of GO-driven similarity (GOS) in comparison to random pairs of genes used as a surrogate for negative interactions. The Biological Process hierarchy provides more reliable results for co-regulatory and protein-protein interactions. GOS represent a relevant resource to support prediction of functional networks in combination with other resources.

基因本体驱动的相似性和功能相互作用的预测集成。
有必要开发方法来自动合并先验知识,以支持新的功能关联的预测和验证。其中一个重要的来源是基因本体(GO)™和标注到GO的基因产物的许多模式生物数据库。我们通过分析酿酒酵母和秀丽隐杆线虫中不同类型的关联,研究了氧化石墨烯驱动的基因相似性及其功能相互作用之间的定量关系。与作为负相互作用替代品的随机基因对相比,相互作用基因表现出明显更高水平的go驱动相似性(GOS)。生物过程层级为共调控和蛋白-蛋白相互作用提供了更可靠的结果。GOS代表了与其他资源相结合的支持功能网络预测的相关资源。
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