Greedy Search-Binary PSO Hybrid for Biclustering Gene Expression Data

Das, Sagnik; Idicula, Sumam Mary

doi:10.5120/651-908

Cited by 22 publications

(12 citation statements)

References 12 publications

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“…To investigate the performance of the novel-version PSO, particle swarm positions are initialized randomly instead of being initialized by some results obtained by other iterative greedy search approaches [18].…”

Section: Resultsmentioning

confidence: 99%

Biclustering of gene expression data using Particle Swarm Optimization integrated with pattern-driven local search

Tian

Jiao

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Biclustering is of great significance in the analysis of gene expression data and is proven to be a NP-hard problem. Among the existing intelligent optimization algorithms used in the gene expression data analysis, most concentrate on the global search ability but ignore the inherent trajectory information of gene expression data, so the search efficiency is low. In this paper, a pattern-driven local search operator is incorporated in the binary Particle Swarm Optimization (PSO) algorithm in order to improve the search efficiency. Experiments show that our approach is valid.

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Section: Resultsmentioning

confidence: 99%

Biclustering of gene expression data using Particle Swarm Optimization integrated with pattern-driven local search

Tian

Jiao

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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“…In contrast to other meta-heuristics descendent methodologies, such as simulated annealing [26], tabu search [50] or particle swarm optimization [51], genetic algorithms start with a set of possible solutions instead of a single one. This characteristic allows genetic algorithms to explore a larger subset of the whole space of solutions, at the same time as it helps them to avoid becoming trapped at a local optimum.…”

Section: Methodsmentioning

confidence: 99%

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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“…The implementation of this algorithm produce optimal number of cluster which results in better analysis of data when compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). Das et al [3] proposed an algorithm based on greedy search and Binary PSO for finding biclusters in gene expression data. In this method, first K-means algorithm is used to cluster rows and columns of the data matrix separately and then they are combined to form small tightly co-regulated submatrices.…”

Section: Related Workmentioning

confidence: 99%

“…There are large number of analysis methods available through which genes are detected that are having the same biologically patterns and common functionality through which it is easy to determine its contribution in many of the biological applications and also helps in medical domain for quick treatment planning, drug discovery, accurate diagnosis as well as prognosis [3]. One of the analysis techniques is clustering which is used to group different types of data into a logical group of clusters which are having similar behavior.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering

Kanungo¹,

Jaiswal²

2015

IJISA

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Abstract-High-throughput microarray technologies have enabled development of robust biclustering algorithms which are capable of discovering relevant local patterns in gene expression datasets wherein subset of genes shows coherent expression patterns under subset of experimental conditions. In this work, we have proposed an algorithm that combines biclustering technique with Particle Swarm Optimization (PSO) structure in order to extract significant biological relevant patterns from such dataset. This algorithm comprises of two phases for extracting biclusters, one is the seed finding phase and another is the seed growing phase. In the seed finding phase, gene clustering and condition clustering is done separately on the gene expression data matrix and the result obtained from both the clustering is combined to form small tightly bound submatrices and those submatrices are used as seeds for the algorithm, which are having the Mean Squared Residue (MSR) value less than the defined threshold value. In the seed growing phase, the number of genes and the number of conditions are added in these seeds to enlarge it by using the PSO structure. It is observed that by using our technique in Yeast Saccharomyces Cerevisiae cell cycle expression dataset, significant biclusters are obtained which are having large volume and less MSR value in comparison to other biclustering algorithms.

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Greedy Search-Binary PSO Hybrid for Biclustering Gene Expression Data

Cited by 22 publications

References 12 publications

Biclustering of gene expression data using Particle Swarm Optimization integrated with pattern-driven local search

Biclustering of gene expression data using Particle Swarm Optimization integrated with pattern-driven local search

Configurable pattern-based evolutionary biclustering of gene expression data

A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering

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