Using a prior biological knowledge of relationships and genetic functions for gene similarity, from repository such as the Gene Ontology (GO), has shown good results in multi-objective gene clustering algorithms. In this scenario and to obtain useful clustering results, it would be helpful to know which measure of biological similarity between genes should be employed to yield meaningful clusters that have both similar expression patterns (co-expression) and biological homogeneity. In this paper, we studied the influence of the four most used GO-based semantic similarity measures in the performance of a multi-objective gene clustering algorithm. We used four publicly available datasets and carried out comparative studies based on performance metrics for the multi-objective optimization field and clustering performance indexes. In most of the cases, using Jiang–Conrath and Wang similarities stand in terms of multi-objective metrics. In clustering properties, Resnik similarity allows to achieve the best values of compactness and separation and therefore of co-expression of groups of genes. Meanwhile, in biological homogeneity, the Wang similarity reports greater number of significant GO terms. However, statistical, visual, and biological significance tests showed that none of the GO-based semantic similarity measures stand out above the rest in order to significantly improve the performance of the multi-objective gene clustering algorithm.

中文翻译：

---

基于go的语义相似性度量对多目标基因聚类算法性能的影响

使用来自诸如基因本体 (GO) 等存储库的基因相似性关系和遗传功能的先验生物学知识，在多目标基因聚类算法中显示出良好的结果。在这种情况下，为了获得有用的聚类结果，了解应该采用哪种基因之间的生物相似性度量来产生具有相似表达模式（共表达）和生物同质性的有意义的聚类，这将是有帮助的。在本文中，我们研究了四种最常用的基于 GO 的语义相似性度量对多目标基因聚类算法性能的影响。我们使用了四个公开可用的数据集，并基于多目标优化领域的性能指标和聚类性能指标进行了比较研究。在大多数情况下，使用江-康拉斯和王的相似性站在多目标指标方面。在聚类特性中，Resnik 相似性允许实现最佳的紧凑性和分离值，从而实现基因组的共表达。同时，在生物同质性方面，Wang 相似性报告了更多重要的 GO 术语。然而，统计、视觉和生物学显着性测试表明，基于 GO 的语义相似性度量中没有一个能显着提高多目标基因聚类算法的性能。在生物同质性方面，Wang 相似性报告了更多重要的 GO 术语。然而，统计、视觉和生物学显着性测试表明，基于 GO 的语义相似性度量中没有一个能显着提高多目标基因聚类算法的性能。在生物同质性方面，Wang 相似性报告了更多重要的 GO 术语。然而，统计、视觉和生物学显着性测试表明，基于 GO 的语义相似性度量中没有一个能显着提高多目标基因聚类算法的性能。