Analysis of Multiple DNA Microarray Datasets

Part of the Springer Handbooks book series (SHB)


In contrast to conventional clustering algorithms, where a single dataset is used to produce a clustering solution, we introduce herein a MapReduce approach for clustering of datasets generated in multiple-experiment settings. It is inspired by the map-reduce functions commonly used in functional programming and consists of two distinctive phases. Initially, the selected clustering algorithm is applied (mapped) to each experiment separately. This produces a list of different clustering solutions, one per experiment. These are further transformed (reduced) by portioning the cluster centers into a single clustering solution. The obtained partition is not disjoint in terms of the different participating genes, and it is further analyzed and refined by applying formal concept analysis.



biological networks gene ontology


deoxyribonucleic acid


dynamic time warping




formal concept analysis


GO category identification


gene ontology


silhouette index


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Department of Computer Systems and TechnologiesTechnical University of Sofia, Branch PlovdivPlovdivBulgaria
  2. 2.Department of ICT & Software EngineeringSirris, The Collective Center for the Belgian Technological IndustryBrusselsBelgium
  3. 3.Department of Computer Systems and TechnologiesTechnical University of Sofia, Plovdiv BranchPlovdivBulgaria

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