High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.

中文翻译：

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高通量基因表达数据的聚类

需要处理、分析和解释高通量生物数据以解决生命科学中的问题。生物信息学、计算生物学和系统生物学使用计算方法处理生物学问题。聚类是用于深入了解生物过程的方法之一，尤其是在基因组学水平上。显然，聚类可用于生物数据分析的许多领域。然而，本文对当前专为分析基因表达数据而设计的聚类算法进行了回顾。它还旨在向运筹学界介绍生物信息学中的主要问题之一——基因表达数据聚类。