ArticleBioData mining2011
Biclustering of gene expression data by correlation-based scatter search.
Article in BioData mining, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Pairwise gene GO-based measures for biclustering of high-dimensional expression data.BioData mining · 2018Article
- Article
- Quality measures for gene expression biclusters.PloS one · 2015Review
- A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.Computational and mathematical methods in medicine · 2015Article
- Biclustering of linear patterns in gene expression data.Journal of computational biology : a journal of computational molecular cell biology · 2012Article
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Authors and funding
3 authors.
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Abstract
backgroundThe analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes.
methodsScatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes.
resultsThe proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database.
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