2011
DOI: 10.1007/978-3-642-24855-9_2
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Biclustering of Expression Microarray Data Using Affinity Propagation

Abstract: Abstract. Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clustering technique to perform biclustering. We extend the CTWC approach, adapting it to Affinity Propagation, by i… Show more

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Cited by 6 publications
(8 citation statements)
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“…As we can see in Fig.2, increasing the noise completely corrupts the performances of the aggregation algorithm. Notice that, obtained results are competitive with other state of the art approaches (see figure 2 in [6], figure 1 in [18] or figure 3 in [9]), confirming the potentialities of the proposed approach.…”
Section: Resultssupporting
confidence: 69%
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“…As we can see in Fig.2, increasing the noise completely corrupts the performances of the aggregation algorithm. Notice that, obtained results are competitive with other state of the art approaches (see figure 2 in [6], figure 1 in [18] or figure 3 in [9]), confirming the potentialities of the proposed approach.…”
Section: Resultssupporting
confidence: 69%
“…In our model we used as similarity the negative of the Euclidean distance (as in [9]), which allows to retrieve only constant value biclusters. As in the original Affinity Propagation model, a proper setting of the preferences (namely the self similarities) is crucial: in our experiments we found that a good choice is represented by the first integer number below the median (which represents the standard setting [1]).…”
Section: Resultsmentioning
confidence: 99%
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