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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Veröffentlicht in:Knowledge and information systems Jg. 35; H. 1; S. 111 - 130
Hauptverfasser: Vukićević, Milan, Kirchner, Kathrin, Delibašić, Boris, Jovanović, Miloš, Ruhland, Johannes, Suknović, Milija
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer-Verlag 01.04.2013
Springer
Springer Nature B.V
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Arrays
Bioinformatics
Biological and medical sciences
Biotechnology
Cluster analysis
Clustering
Computer Science
Computer science; control theory; systems
Data analysis
Data mining
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Database Management
Datasets
Design engineering
Diagnostic systems
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Gene expression
Genes
Genetic engineering
Genetic technics
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Memory organisation. Data processing
Methods. Procedures. Technologies
Microbiology
Molecular and cellular biology
Molecular genetics
Regular Paper
Software
Studies
Tasks
Theoretical computing
Component-based algorithms
Clustering
Bioinformatics
Microarray data
Microbiology
Competitiveness
DNA chip
Algorithmics
Cluster
Information retrieval
Software component
Data mining
Computer aided design
ISSN:0219-1377, 0219-3116
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-012-0542-5