Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.

中文翻译：

---

TriRNSC：使用受限邻域搜索对基因表达微阵列数据进行三聚类。

微阵列数据的计算分析对于理解基因行为和得出有意义的结果至关重要。在无监督域中对基因表达微阵列数据进行聚类和双聚类非常重要，因为它们的结果在许多方面直接主导了医疗保健研究。然而，当时间因子作为第三维添加到微阵列数据集时，这些方法会失败。可以使用三聚类分析这个三维数据集，该三聚类发现在特定时间点的条件子集下追求相同行为的相似基因集。本手稿中提出了一种新的三聚类算法 (TriRNSC)，以发现基因表达谱中有意义的三聚类。TriRNSC 基于受限邻域搜索聚类 (RNSC)，一种流行的基于图的聚类方法，考虑了基因、实验条件和实例的时间点。所提出的算法的性能是根据体积和一些性能指标来评估的。基因本体和 KEGG 通路分析用于生物学验证 TriRNSC 结果。TriRNSC 的效率表明了它的能力和可靠性，也证明了它相对于其他最先进方案的可用性。所提出的框架启动了 RNSC 算法在基因表达谱的三聚类中的应用。基因本体和 KEGG 通路分析用于生物学验证 TriRNSC 结果。TriRNSC 的效率表明了它的能力和可靠性，也证明了它相对于其他最先进方案的可用性。所提出的框架启动了 RNSC 算法在基因表达谱的三聚类中的应用。基因本体和 KEGG 通路分析用于生物学验证 TriRNSC 结果。TriRNSC 的效率表明了它的能力和可靠性，也证明了它相对于其他最先进方案的可用性。所提出的框架启动了 RNSC 算法在基因表达谱的三聚类中的应用。