基于粗粒度并行遗传算法的基因表达微阵列数据聚类
Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic Algorithm
摘要：
微阵列数据分析是计算生物学的主要研究领域之一。许多技术，如聚类，biclustering经常应用于微阵列数据提取有意义的结果，发挥关键作用，在实际的医疗保健事务，如疾病识别，药物发现等，但这些技术变得过时时，作为另一个因素被认为是在这样的数据进行评估。这一问题激发了对基因表达3D微阵列数据进行三聚类的研究。在这篇文章中，提出了一种新的方法的基础上粗粒度并行遗传方法来定位有意义的三聚体基因表达数据。结果是相当令人印象深刻的，因为它们是更有效的相比，传统的最先进的遗传方法，以前应用于三聚体的三维GCT微阵列数据。
英文摘要：
Microarray data analysis is one of the major area of research in the field computational biology. Numerous techniques like clustering, biclustering are often applied to microarray data to extract meaningful outcomes which play key roles in practical healthcare affairs like disease identification, drug discovery etc. But these techniques become obsolete when time as an another factor is considered for evaluation in such data. This problem motivates to use triclustering method on gene expression 3D microarray data. In this article, a new methodology based on coarse-grained parallel genetic approach is proposed to locate meaningful triclusters in gene expression data. The outcomes are quite impressive as they are more effective as compared to traditional state of the art genetic approaches previously applied for triclustering of 3D GCT microarray data.
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