Evolutionary Biclustering Algorithm of Gene Expression Data

Ayadi, Wassim; Maatouk, Ons; Bouziri, Hend

doi:10.1109/dexa.2012.46

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…An evolutionary biclustering algorithm (EvoBic) with a variable-length representation was proposed by Ayadi et al in [3]. This integer encoding represents the individuals as a string composed of ordered genes and conditions indices, reducing time and the memory space [8].…”

Section: Yearmentioning

confidence: 99%

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: Yearmentioning

confidence: 99%

Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

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“…where r is the expected minimal number of rows that should be included within a bicluster B ¼ I; J ð Þ, with its rows and columns denoted as I and J, respectively. EBIC uses a different representation compared with other evolutionary-based biclustering methods (Ayadi et al, 2012;Divina and Aguilar-Ruiz, 2006;Mitra and Banka, 2006). Instead of modeling a bicluster as a tuple with a set of rows and a set of columns, biclusters in EBIC are represented by a series of column indices.…”

Section: Motivationmentioning

confidence: 99%

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery

et al. 2018

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“…EBIC uses a different representation compared with other evolutionary-based biclustering methods (Divina and Aguilar-Ruiz, 2006;Mitra and Banka, 2006;Ayadi et al, 2012). Instead of modeling a bicluster as a tuple with a set of rows and a set of columns, biclusters in EBIC are represented by a series of column indices.…”

Section: Motivationmentioning

confidence: 99%

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover

Orzechowski,

Sipper,

Huang

et al. 2018

Preprint

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Motivation: Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. Results: In this paper a novel biclustering algorithm based on evolutionary computation, a subfield of artificial intelligence (AI), is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms.

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Evolutionary Biclustering Algorithm of Gene Expression Data

Cited by 9 publications

References 18 publications

Biclustering Algorithms Based on Metaheuristics: A Review

Biclustering Algorithms Based on Metaheuristics: A Review

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover

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