alexa Abstract | An Effective Validation Methodology of Proximity Measures for Clustering Gene Expression Microarray Data
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access

Abstract

The countenance level measurement for thousands of genes is allowed in a parallel fashion by microarray 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 selecting a clustering algorithm for attaining reasonable clustering results. Til today, there are no inclusive guidelines regarding how to elect proximity measures for clustering microarray data. The choice of proximity measures is studied for the clustering of microarray data by estimating the performance of twelve proximity measures in some data sets from time course 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 and collected data sets from the microarray literature in a benchmark is used along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.

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Author(s): Mahima KM, Govindaraj M

Keywords

Gene expression microarray data, proximity measures, Intrinsic biological separation ability, Method Validation

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