Finding best algorithmic components for clustering microarray data
The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this pr...
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| Vydáno v: | Knowledge and information systems Ročník 35; číslo 1; s. 111 - 130 |
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| Jazyk: | angličtina |
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Springer-Verlag
01.04.2013
Springer Springer Nature B.V |
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| ISSN: | 0219-1377, 0219-3116 |
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| Abstract | The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms. |
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| AbstractList | The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms. The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.[PUBLICATION ABSTRACT] |
| Author | Vukićević, Milan Kirchner, Kathrin Ruhland, Johannes Jovanović, Miloš Suknović, Milija Delibašić, Boris |
| Author_xml | – sequence: 1 givenname: Milan surname: Vukićević fullname: Vukićević, Milan email: vukicevicm@fon.bg.ac.rs organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 2 givenname: Kathrin surname: Kirchner fullname: Kirchner, Kathrin organization: Faculty of Economics and Business Administration, Friedrich Schiller University of Jena – sequence: 3 givenname: Boris surname: Delibašić fullname: Delibašić, Boris organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 4 givenname: Miloš surname: Jovanović fullname: Jovanović, Miloš organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 5 givenname: Johannes surname: Ruhland fullname: Ruhland, Johannes organization: Faculty of Economics and Business Administration, Friedrich Schiller University of Jena – sequence: 6 givenname: Milija surname: Suknović fullname: Suknović, Milija organization: Faculty of Organizational Sciences, University of Belgrade |
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| Keywords | Component-based algorithms Clustering Bioinformatics Microarray data Microbiology Competitiveness DNA chip Algorithmics Cluster Information retrieval Software component Data mining Computer aided design |
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