GOGA:基于go驱动遗传算法的基因表达数据模糊聚类

A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, B. Brors
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引用次数: 3

摘要

本文提出了一种基于遗传算法的模糊聚类方法(GOGA),该方法将基因本体(Gene Ontology, GO)知识融入到聚类过程中,用于聚类微阵列基因表达数据。为此,提出的技术结合了基于表达和基于go的基因不相似性测量。适应度函数中包含了基于表达式和基于go的聚类目标。所提出的技术的性能已经证明了现实生活中的酵母细胞周期数据集。为了验证聚类结果,已经进行了基于KEGG途径的富集研究。
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GOGA: GO-driven Genetic Algorithm-based fuzzy clustering of gene expression data
In this article, a Genetic Algorithm-based fuzzy clustering method (GOGA), which incorporates Gene Ontology (GO) knowledge in the clustering process, has been proposed for clustering microarray gene expression data. The proposed technique combines the expression-based and GO-based gene dissimilarity measures for this purpose. Both expression-based and GO-based clustering objectives have been incorporated in the fitness function. The performance of the proposed technique has been demonstrated on real-life Yeast Cell Cycle data set. KEGG pathway based enrichment studies have been conducted for validating the clustering results.
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