Evolutionary biclustering algorithms: an experimental study on microarray data

Maatouk, Ons; Ayadi, Wassim; Bouziri, Hend; Duval, Béatrice

doi:10.1007/s00500-018-3394-4

Cited by 14 publications

(6 citation statements)

References 76 publications

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“…Bicluster crossover operator [48] The crossover is based on the following four steps: (i) creation of the merge bicluster B merge from two parent biclusters (individuals); (ii) discretization of the merge bicluster, B discrete ; (iii) construction of the variation matrix, M var ; (iv) extraction of child biclusters.…”

Section: Crossover Operatorsmentioning

confidence: 99%

“…In EBACross, a binary encoding of fixed length, a crossover method based on the standard deviation of the biclusters, and a mutation strategy based on the biclusters' coherence are considered. Later, the same authors proposed a generic evolutionary biclustering algorithm (EBA) [48]. In this work, the authors analyzed the EBA's performance by varying its genetic components.…”

Section: Yearmentioning

confidence: 99%

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Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

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Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters is an NP-hard problem that can be formulated as an optimization problem. Therefore, different metaheuristics have been applied to biclustering problems because of their exploratory capability of solving complex optimization problems in reasonable computation time. Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics. This chapter will present a survey of metaheuristics approaches to address the biclustering problem. The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators. A specific discussion on single versus multi-objective approaches is presented. Finally, some emerging research directions are presented.

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Section: Crossover Operatorsmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

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“…The recent development of biological experiments has generated vast amounts of gene expression data. Thus, comprehending and interpreting the enormous number of genes has become a significant challenge ( Diniz et al, 2019 ; Maâtouk et al, 2019 ; Li and Yang, 2020 ; Summers et al, 2020 ; Nisar et al, 2021 ; Dang et al, 2022 ). Semi-supervised learning ( Chapelle et al, 2006 ) is a focused issue in the analysis of gene expression data, the research branches mainly include semi-supervised gene clustering ( Yu et al, 2014 ; Yu et al, 2016 ; Xia et al, 2018 ; Liu et al, 2021 ), semi-supervised gene classification ( Huang and Feng, 2012 ; Zhang et al, 2021 ), semi-supervised gene selection ( Mahendran et al, 2020 ), and semi-supervised gene dimensionality reduction ( Feng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Semi-supervised learning ( Chapelle et al, 2006 ) is a focused issue in the analysis of gene expression data, the research branches mainly include semi-supervised gene clustering ( Yu et al, 2014 ; Yu et al, 2016 ; Xia et al, 2018 ; Liu et al, 2021 ), semi-supervised gene classification ( Huang and Feng, 2012 ; Zhang et al, 2021 ), semi-supervised gene selection ( Mahendran et al, 2020 ), and semi-supervised gene dimensionality reduction ( Feng et al, 2021 ). In this paper, we focus on the semi-supervised gene clustering problem for identify co-expressed gene groups, which can provide a useful basis for the further investigation of gene function and gene regulation in the field of functional genomics ( Maâtouk et al, 2019 ). When clustering gene expression data, practical dataset usually exists in the form of a large amount of unlabeled data and a small amount of labeled data.…”

Section: Introductionmentioning

confidence: 99%

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data

Liang

Pan

et al. 2023

Front. Genet.

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Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label confidence to better guide the partition of unlabeled data. The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. Compared with a variety of state-of-the-art unsupervised and semi-supervised learning algorithms, the SSCAC algorithm has demonstrated its superiority through extensive experiments on two benchmark gene expression datasets.

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“…Advantage: The ECT is effectively remove the noise, maximum consistency, and more correspondence clusters. Disadvantage: In high dimensional data, maximum time is required for searching the optimal solution in search space[15][16].• Ensemble Clustering Technique: It is a popular way of combining the classification strategies to overcome instabilities in different classification algorithms. It scales linearly among the number of data points and the number of repetitions by making it feasible to apply for large data sets.…”

mentioning

confidence: 99%

Review on Clustering Cancer Genes

Prabhuraj¹,

Shastry²,

Patil³

2019

IJCET

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Present studies, development of genomic technologies are highly concentrated on galactic scale gene data. In Bioinformatics community, the sizable volume of gene data investigation and distinguishing the behavior of genes in antithetical conditions are the intriguing task. This cognitive factor can be deal by the clustering technique, its groups the similarity patterns at various features. Moreover, gene expression data indicates the contrastive levels of gene behaviors in various tissue cells and it does provide the feature information effectively. This gene clustering investigation is precise and accommodating in cancer uncovering because of its easiness to detect the cancerous and non-cancerous genes. The precautionary measures cancer diagnostic is precise crucial for cancer prevention and treatment. The existing cancer gene clustering techniques includes several limitations such as time complexities in training and testing samples, maximum redundant features and high dimensional data. These issues are severely influences the data clustering accuracy. This paper focuses on survey of various clustering techniques of cancer gene clustering with respect to cancer gene benchmark datasets. Furthermore, review of existing cancer gene clustering technique describes the advantages and limitations comprehensively.

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Evolutionary biclustering algorithms: an experimental study on microarray data

Cited by 14 publications

References 76 publications

Biclustering Algorithms Based on Metaheuristics: A Review

Biclustering Algorithms Based on Metaheuristics: A Review

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data

Review on Clustering Cancer Genes

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