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首页> 外文期刊>International Journal of Innovative Computing Information and Control >DYNAMIC CORE BASED CLUSTERING OF GENE EXPRESSION DATA
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DYNAMIC CORE BASED CLUSTERING OF GENE EXPRESSION DATA

机译：基于动态核心的基因表达数据聚类

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摘要

Modern microarray technology allows measuring the expression levels of thousands of genes, under different environmental conditions and over time. Clustering is, often, a first step in the analysis of the huge amounts of biological data obtained from these microarray based experiments. As most biological processes are dynamic and biological experiments are conducted during longer periods of time, the data is continuously subject to change and researchers must either wait until the end of the experiments to have all the necessary information, or analyze the data gradually, as the experiment progresses. If the available data is clustered progressively, using clustering algorithms, as soon as new data emerges, the algorithm must be run from scratch, thus leading to delayed results. In this paper, we approach the problem of dynamic gene expression data sets and we propose a dynamic core based clustering algorithm, which can handle newly collected data, by starting from a previously obtained partition, without the need to rerun the algorithm from the beginning. The experimental evaluation is performed on a real-life gene expression data set and the algorithm has proven to perform well in terms of a series of evaluation measures.
机译：现代微阵列技术可在不同的环境条件下并随时间测量数千种基因的表达水平。聚类通常是分析从这些基于微阵列的实验中获得的大量生物学数据的第一步。由于大多数生物过程是动态的，并且生物实验是在较长的时间内进行的，因此数据会不断变化，研究人员必须等到实验结束后才能获得所有必要的信息，或者逐步分析数据，因为实验进度。如果使用聚类算法逐步将可用数据聚类，则一旦出现新数据，就必须从头开始运行该算法，从而导致结果延迟。在本文中，我们解决了动态基因表达数据集的问题，并提出了一种基于动态核心的聚类算法，该算法可以从先前获得的分区开始处理新收集的数据，而无需从头开始重新运行该算法。实验评估是在现实生活中的基因表达数据集上进行的，并且该算法在一系列评估措施方面已证明表现良好。

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