文章基本信息

标题：An Effective Validation Methodology of Proximity Measures for Clustering Gene Expression Microarray Data
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作者：Mahima KM ; Govindaraj M
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：2
DOI：10.15680/ijircce.2015.0302009
出版社：S&S Publications
摘要：The countenance level measurement for thousands of genes is allowed in a parallel fashion bymicroarray technology. Clustering is one of the first stage accepted to reveal information from gene expression data.Choosing an appropriate proximity measure (similarity or distance) is having great significance in addition to selectinga clustering algorithm for attaining reasonable clustering results. Til today, there are no inclusive guidelines regardinghow to elect proximity measures for clustering microarray data. The choice of proximity measures is studied for theclustering of microarray data by estimating the performance of twelve proximity measures in some data sets from timecourse and cancer experiments. Given that different measures hoisted out for time course and cancer data evaluations,their choice should be specific to each scenario. To estimate measures on time-course data, the pre-processed andcollected data sets from the microarray literature in a benchmark is used along with a new methodology, called IntrinsicBiological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of newmeasures for gene time-course data.
关键词：Gene expression microarray data; proximity measures; Intrinsic biological separation ability