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A New Path Based Hybrid Measurefor Gene Ontology Similarity

机译：基于新路径的基因本体相似度混合度量

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摘要

Gene Ontology (GO) consists of a controlled vocabulary of terms, annotating a gene or gene product, structured in a directed acyclic graph. In the graph, semantic relations connect the terms, that represent the knowledge of functional description and cellular component information of gene products. GO similarity gives us a numerical representation of biological relationship between a gene set, which can be used to infer various biological facts such as protein interaction, structural similarity, gene clustering, etc. Here we introduce a new shortest path based hybrid measure of ontological similarity between two terms which combines both structure of the GO graph and information content of the terms. Here the similarity between two terms $t_1$ and $t_2$, referred to as $GOSim_{PBHM}(t_1,t_2)$, has two components; one obtained from the common ancestors of $t_1$ and $t_2$. The other from their remaining ancestors. The proposed path based hybrid measure does not suffer from the well-known shallow annotation problem. Its superiority with respect to some other popular measures is established for protein protein interaction prediction, correlation with gene expression and functional classification of genes in a biological pathway. Finally, the proposed measure is utilized to compute the average GO similarity score among the genes that are experimentally validated targets of some microRNAs. Results demonstrate that the targets of a given miRNA have a high degree of similarity in the biological process category of GO.
机译：基因本体论（GO）由受控的词汇表组成，这些词汇表述有向基因或基因产物，并以有向无环图的形式构造。在图中，语义关系将术语联系起来，这些术语代表功能描述的知识和基因产物的细胞成分信息。 GO相似性为我们提供了一个基因组之间生物学关系的数值表示，可用于推断各种生物学事实，例如蛋白质相互作用，结构相似性，基因聚类等。在这里，我们介绍一种基于最短路径的新的本体相似性混合测量两个术语之间的组合，结合了GO图形的结构和这些术语的信息内容。这里，两个术语$ t_1 $和$ t_2 $之间的相似性，称为$ GOSim_ {PBHM}（t_1，t_2）$，具有两个成分；一个是从共同祖先$ t_1 $和$ t_2 $获得的。另一个来自他们的剩余祖先。所提出的基于路径的混合度量不存在众所周知的浅注释问题。在蛋白质相互作用预测，与基因表达的相关性以及生物学途径中基因的功能分类方面，相对于其他一些流行的方法，它的优越性得以确立。最后，所提出的措施可用于计算基因中的平均GO相似性得分，这些基因是一些microRNA的实验验证靶标。结果表明，给定miRNA的靶标在GO的生物过程类别中具有高度相似性。

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