• Graph-based unsupervised feature selection and multiview clustering for microarray data

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    • Keywords


      Biological functional enrichment; clustering; explorative data analysis; feature selection; gene selection; graph-based learning.

    • Abstract


      A challenge in bioinformatics is to analyse volumes of gene expression data generated through microarray experiments and obtain useful information. Consequently, most microarray studies demand complex data analysis to infer biologically meaningful information from such high-throughput data. Selection of informative genes is an important data analysis step to identify a set of genes which can further help in finding the biological information embedded in microarray data, and thus assists in diagnosis, prognosis and treatment of the disease. In this article we present an unsupervised feature selection technique which attempts to address the goal of explorative data analysis, unfolding the multi-faceted nature of data. It focuses on extracting multiple clustering views considering the diversity of each view from high-dimensional data. We evaluated our technique on benchmark data sets and the experimental results indicates the potential and effectiveness of the proposed model in comparison to the traditional single view clustering models, as well as other existing methods used in the literature for the studied datasets.

    • Author Affiliations


      Tripti Swarnkar1 2 Pabitra Mitra1

      1. Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur 721 302, India
      2. Institute of Technical Education & Research, Siksha `O' Anusandhan University, Bhubaneswar 751 030, India
    • Dates

  • Journal of Biosciences | News

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      Posted on July 25, 2019

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