A new method to measure the semantic similarity of GO terms
A new method to measure the semantic similarity of GO terms
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DOI：
10.1093/bioinformatics/btm087
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发表时间：
2007-05-15
期刊：
影响因子：
5.8
通讯作者：
Chen, Chin-Fu
中科院分区：
文献类型：
--
作者：
Wang, James Z.;Du, Zhidian;Chen, Chin-Fu
动机：虽然受控的生化或生物学词汇，如基因本体（GO） （http://www.geneontology.org），解决了在不同数据源中对基因的一致描述的需要，但仍然没有有效的方法来确定基于异构数据源的基因注释信息的基因功能相似性。结果：为了解决这一关键需求，我们提出了一种新的方法，通过聚合GO图中祖先术语（包括该特定术语）的语义贡献，将GO术语的语义（生物学意义）编码为数值，然后设计了一种算法来测量GO术语的语义相似性。基于基因标注中GO术语的语义相似度，设计了一种新的基因功能相似度度量算法。利用我们的算法测量从酵母菌基因组数据库（SGD）中检索到的通路中基因的功能相似性的结果，以及基于我们的算法获得的相似性值对这些基因进行聚类的结果显示与人类的观点一致。此外，我们还开发了一套用于基因相似性测量和知识发现的在线工具。
Motivation: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.Results: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.