2015
DOI: 10.1007/s12038-015-9559-8
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Graph-based unsupervised feature selection and multiview clustering for microarray data

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… Show more

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Cited by 12 publications
(6 citation statements)
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References 39 publications
(46 reference statements)
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“…A good survey on existing research on the development of multi-view clustering algorithms can be found in [13,28]. On the problem of gene selection, a graph-theoretic multi-view clustering on gene expression data was proposed in [29]. Though their proposed algorithm is a multi-view approach, the views are developed based on expression data, and no other genomic/proteomic resources have been taken into account.…”
Section: Related Work and Motivationmentioning
confidence: 99%
“…A good survey on existing research on the development of multi-view clustering algorithms can be found in [13,28]. On the problem of gene selection, a graph-theoretic multi-view clustering on gene expression data was proposed in [29]. Though their proposed algorithm is a multi-view approach, the views are developed based on expression data, and no other genomic/proteomic resources have been taken into account.…”
Section: Related Work and Motivationmentioning
confidence: 99%
“…In Liu et al [18] and Xue et al [19], authors have developed feature selection algorithms using multi view data for video, text, and image data sets. On the problem of gene selection, an unsupervised graph-theoretic multi-view clustering approach was proposed bt Swarnkar et al [20]. Though their proposed algorithm is a multi-view approach, here, different 'views' are developed by considering different feature subspaces over a gene expression data set.…”
Section: Related Work and Motivationmentioning
confidence: 99%
“…Liu et al [13] modeled multiview data as a tensor and developed a new tensor-based framework for the integration of heterogeneous multiview data in the context of spectral clustering. More recently, new learning technologies [14][15][16][17][18][19] have been introduced for multi-view data analysis and processing. When seeking to achieve consistent and common conclusions, these learning models also take into account the differences among multiple views.…”
Section: Introductionmentioning
confidence: 99%