Optimisation algorithms for microarray biclustering
Data(s) |
2013
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Resumo |
In providing simultaneous information on expression profiles for thousands of genes, microarray technologies have, in recent years, been largely used to investigate mechanisms of gene expression. Clustering and classification of such data can, indeed, highlight patterns and provide insight on biological processes. A common approach is to consider genes and samples of microarray datasets as nodes in a bipartite graphs, where edges are weighted e.g. based on the expression levels. In this paper, using a previously-evaluated weighting scheme, we focus on search algorithms and evaluate, in the context of biclustering, several variations of Genetic Algorithms. We also introduce a new heuristic “Propagate”, which consists in recursively evaluating neighbour solutions with one more or one less active conditions. The results obtained on three well-known datasets show that, for a given weighting scheme,optimal or near-optimal solutions can be identified. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/82684/1/82684.pdf DOI:10.1109/EMBC.2013.6609569 Perrin, Dimitri & Duhamel, Christophe (2013) Optimisation algorithms for microarray biclustering. In Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Osaka, Japan, pp. 592-595. |
Direitos |
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Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Tipo |
Conference Paper |