标题： 基于基因的基因本体论的语义结构作为复杂网络 显示英文标题

标题： Gene-based and semantic structure of the Gene Ontology as a complex network

摘要： 过去十年见证了基于本体的工具的出现和巩固，用于识别和生物解释基因类，例如基因本体（Gene Ontology）。 随着时间的推移积累的信息并包含在GO中的信息被编码在术语的定义中以及术语之间的语义关系设置中。 这种方法可以通过基于基因之间关系知识的自下而上方法进行有益的补充。 为此，我们从复杂网络的角度研究基因本体。 我们考虑由基因本体联盟提供的语义关系自然关联的术语语义网络，以及一个基于基因的加权网络，其中节点是术语，当两个术语都被基因注释时，会在它们之间建立链接。 本文的一个目的是了解语义网络和基于基因的网络是否具有相同的结构特性。 然后我们考虑网络社区。 因此，SVNs网络中社区的识别可以成为一种简单协议的基础，旨在充分利用术语之间的可能关系，从而改善对GO语义结构的理解。 从生物医学的角度来看，这也很重要，因为它可能揭示在某个术语中过度表达的基因如何影响其他由GO语义不直接连接的生物功能。 作为副产品，我们提出了一种简单的方法，可以初步了解GO术语组的生物学特征。 显示英文摘要

摘要： The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. This approach might be usefully complemented by a bottom-up approach based on the knowledge of relationships amongst genes. To this end, we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium and a gene-based weighted network in which the nodes are the terms and a link between any two terms is set up whenever genes are annotated in both terms. One aim of the present paper is to understand whether the semantic and the gene-based network share the same structural properties or not. We then consider network communities. The identification of communities in the SVNs network can therefore be the basis of a simple protocol aiming at fully exploiting the possible relationships amongst terms, thus improving the knowledge of the semantic structure of GO. This is also important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological functions not directly linked by the GO semantics. As a by-product, we present a simple methodology that allows to have a first glance insight about the biological characterization of groups of GO terms.