EXCLUVIS：一个用于基因表达数据聚类和可视化比较研究的MATLAB GUI软件
EXCLUVIS: A MATLAB GUI Software for Comparative Study of Clustering and Visualization of Gene Expression Data
摘要：
聚类是一种流行的数据采掘技术，旨在将输入空间划分为多个同质区域。在文献中存在几种聚类算法。聚类算法的性能取决于它的输入参数，这些参数可以实质上影响算法的行为。聚类有效性索引确定最适合底层数据的分区。在生物信息学中，微阵列基因表达技术使得同时测量数千个基因的基因表达水平成为可能。许多基因组学研究，其目的是分析一些基因的功能，高度依赖于一些聚类技术，用于将相似表达的基因分组在一个簇中或基于基因的相似表达值划分组织样本。在这项工作中，一个应用程序包被称为EXCLUVIS（基因表达数据聚类和可视化）已开发使用MATLAB图形用户界面（GUI）环境分析不同的聚类算法的性能基因表达数据集。在这个应用程序包中，用户需要选择一些参数，如内部有效性指标，外部有效性指标和集群的数量从活动窗口的聚类算法的性能进行评估。EXCLUVIS比较了K-means、模糊C-means、层次聚类和多目标进化聚类算法的性能。热图和聚类图用于可视化结果。EXCLUVIS允许用户轻松地找到聚类解决方案的优点，并提供聚类结果的可视化表示。
英文摘要：
Clustering is a popular data mining technique that aims to partition an input space into multiple homogeneous regions. There exist several clustering algorithms in the literature. The performance of a clustering algorithm depends on its input parameters which can substantially affect the behavior of the algorithm. Cluster validity indices determine the partitioning that best fits the underlying data. In bioinformatics, microarray gene expression technology has made it possible to measure the gene expression levels of thousands of genes simultaneously. Many genomic studies, which aim to analyze the functions of some genes, highly rely on some clustering technique for grouping similarly expressed genes in one cluster or partitioning tissue samples based on similar expression values of genes. In this work, an application package called EXCLUVIS (gene EXpression data CLUstering and VISualization) has been developed using MATLAB Graphical User Interface (GUI) environment for analyzing the performances of different clustering algorithms on gene expression datasets. In this application package, the user needs to select a number of parameters such as internal validity indices, external validity indices and number of clusters from the active windows for evaluating the performance of the clustering algorithms. EXCLUVIS compares the performances of K-means, fuzzy C-means, hierarchical clustering and multiobjective evolutionary clustering algorithms. Heatmap and cluster profile plots are used for visualizing the results. EXCLUVIS allows the users to easily find the goodness of clustering solutions as well as provides visual representations of the clustering outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
