An effective measure for assessing the quality of biclusters

Divina, Federico; Pontes, Beatriz; Giráldez, Raúl; Aguilar–Ruiz, Jesús S.

doi:10.1016/j.compbiomed.2011.11.015

Cited by 41 publications

(32 citation statements)

References 43 publications

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“…Other evolutionary biclustering approaches have adopted a totally random initial population generation [54], where initial solutions are made up of a random number of elements (genes and samples) randomly chosen from the microarray, or also random strategies in which the chromosomes are made up of only one element (one gene and one condition) from the microarray [32]. In our experimental tests on synthetic data, we found that these kind of initializations did not give the algorithm an initial space solution good enough to come up to the best solution.…”

Section: Methodsmentioning

confidence: 99%

“…The basic idea behind the Virtual Error (VE) [32] is to measure how genes in a bicluster follow the general tendency within the group. In order to catch the general tendency of the genes across the conditions contained in the bicluster, a new gene (the so-called virtual gene ) is computed as the mean of all the genes in the bicluster.…”

Section: Introductionmentioning

confidence: 99%

“…The more similar the genes are, the lower the value for VE. In fact, VE has proven to be 0 for those biclusters presenting either shifting or scaling patterns [32]. It has also been proven that when the data in a bicluster resembles a perfect pattern but containing some other noise data, VE will have a greater value depending on the amount of noise data in the bicluster [33].…”

Section: Introductionmentioning

confidence: 99%

“…It has also been proven that when the data in a bicluster resembles a perfect pattern but containing some other noise data, VE will have a greater value depending on the amount of noise data in the bicluster [33]. VE has been used in an evolutionary search strategy producing satisfactory results and improving those obtained with other evaluation measures [32], being able to recognized both shifting and scaling patters, though no simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Configurable pattern-based evolutionary biclustering of gene expression data

Pontes

Giráldez

Aguilar–Ruiz

2013

Algorithms Mol Biol

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BackgroundBiclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties.ResultsHere, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns).ConclusionsWe have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Configurable pattern-based evolutionary biclustering of gene expression data

Pontes

Giráldez

Aguilar–Ruiz

2013

Algorithms Mol Biol

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“…VE [29] follows similar assumptions regarding the standardisation process within the evaluation as MSA. The basic idea behind VE is to measure how genes follow the general tendency within the bicluster.…”

Section: Bicluster Evaluation Measuresmentioning

confidence: 99%

Quality Measures for Gene Expression Biclusters

2015

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An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters.

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Unsupervised Algorithms for Microarray Sample Stratification

Fratello

Cattelani

Federico

et al. 2021

Methods in Molecular Biology

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The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub-)group discovery.

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An effective measure for assessing the quality of biclusters

Cited by 41 publications

References 43 publications

Configurable pattern-based evolutionary biclustering of gene expression data

Configurable pattern-based evolutionary biclustering of gene expression data

Quality Measures for Gene Expression Biclusters

Unsupervised Algorithms for Microarray Sample Stratification

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