ArticleBMC bioinformatics2012
Pattern-driven neighborhood search for biclustering of microarray data.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Unsupervised Algorithms for Microarray Sample Stratification.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis.BMC bioinformatics · 2014Article
- Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion.BMC genomics · 2013Article
- Introduction: Advanced intelligent computing theories and their applications in bioinformatics.BMC bioinformatics · 2012Article
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3 authors.
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Abstract
backgroundBiclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods.
resultsWe describe a stochastic pattern-driven neighborhood search algorithm for the biclustering problem. Starting from an initial bicluster, the proposed method improves progressively the quality of the bicluster by adjusting some genes and conditions. The adjustments are based on the quality of each gene and condition with respect to the bicluster and the initial data matrix. The performance of the method was evaluated on two well-known microarray datasets (Yeast cell cycle and Saccharomyces cerevisiae), showing that it is able to obtain statistically and biologically significant biclusters. The proposed method was also compared with six reference methods from the literature.
conclusionsThe proposed method is computationally fast and can be applied to discover significant biclusters. It can also used to effectively improve the quality of existing biclusters provided by other biclustering methods.
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