Abstract
In the context of microarray data analysis, biclustering aims to identify simultaneously a group of genes that are highly correlated across a group of experimental conditions. This paper presents a Biclustering Iterative Local Search (BILS) algorithm to the problem of biclustering of microarray data. The proposed algorithm is highlighted by the use of some original features including a new evaluation function, a dedicated neighborhood relation and a tailored perturbation strategy. The BILS algorithm is assessed on the well-known yeast cell-cycle dataset and compared with two most popular algorithms. © 2010 Springer-Verlag.
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Ayadi, W., Elloumi, M., & Hao, J. K. (2010). Iterated local search for biclustering of microarray data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6282 LNBI, pp. 219–229). https://doi.org/10.1007/978-3-642-16001-1_19
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