Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data

Das, Sagnik; Idicula, Sumam Mary

doi:10.5120/650-907

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…Local search starts from the Bicluster generated in the construction stage, where the local search finds a new bicluster in the field of the current bicluster and replaces the current bicluster when it has a lower Hscore . The algorithm pseudocode is shown in Table 1 [15,16]. The GRASP-based biclustering algorithm generates high quality bicluster seeds by the K-means clustering method, so the bicluster seeds play a decisive role in the final result of bicluster.…”

Section: Analysis Of Biclustering Algorithm Based On Greedy Randomizementioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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The biclustering algorithm based on greedy randomized adaptive search procedure (GRASP) has two main steps, firstly, bicluster seeds are generated by Kmeans clustering; secondly, the greedy randomized adaptive search procedure is used to expand these bicluster seeds so that to get some better biclusters. However, K-means clustering assumes that each gene belongs to only one cluster, which is not biologically valid, because some genes may not belong to only one category, and K-means clustering requires multiple runs to determine the number of "clusters," all of which will affect the identification of bicluster seeds. This paper optimizes GRASP-based bIclustering algorithm. In this paper, the single column vector clustering is used to generate biclusters seed, and then the bicluster seeds are added more rows and columns to generate the final biclusters. Experiment shows that this algorithm can generate a large number of biclusters with coexpression level, and the algorithm also finds more biclusters.

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Section: Analysis Of Biclustering Algorithm Based On Greedy Randomizementioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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“…The introduction of gene expression profiling techniques such as DNA microarray has made it possible to simultaneously analyze expression levels for thousands of genes under a number of different conditions [1]. Gene expression data is usually arranged in the form of a matrix, in which each row corresponds to a gene, each column corresponds to a condition and each element represents an expression level of a gene under a condition [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Biclustering of Gene Expression Data using a Two - Phase Method

Das¹,

Borah²

2014

IJCA

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Biclustering is a very useful data mining technique which identifies coherent patterns from microarray gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. Biclustering is a powerful analytical tool for the biologist and has generated considerable interest over the past few decades. Many biclustering algorithms optimize a mean squared residue to discover biclusters from a gene expression dataset. In this paper a Two-Phase method of finding a bicluster is developed. In the first phase, a modified version of k-means algorithm is applied to the gene expression data to generate k clusters. In the second phase, an iterative search is performed to check the possibility of removing more genes and conditions within the given threshold value of mean squared residue score. Experimental results on yeast dataset show that our approach can effectively find high quality biclusters

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“…Shyama Das et al [13] proposed a greedy randomized adaptive search procedure to find the biclusters. The bicluster seeds are generated using k-means algorithm, and then these seed are enlarged using GRASP.…”

Section: Related Workmentioning

confidence: 99%

“…Microarray operations are done under different condition to have parallel comparison between the experimental levels of gene. The relative abundance of mRNA of a gene is called the expression level of a gene [9].This is measured using DNA microarray technology which revolutionized the gene expression study by simultaneously measuring the expression levels of thousands of genes in a single experiment [8] [13].…”

Section: A Microarray or Gene Expression Datamentioning

confidence: 99%

“…Genes participating in the same biological process will have similar expression patterns. Clustering is the suitable mining method for identifying these patterns [1] [13]. The ability of arrays to monitor thousands of separate but unrelated events simultaneously has captured the thoughts of scientists practicing in both basic and applied research [8][16] [17].…”

Section: A Microarray or Gene Expression Datamentioning

confidence: 99%

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Randomized Algorithmic Approach for Biclustering of Gene Expression Data

Nayak¹,

Mishra

Das³

et al. 2010

IJACSA

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Abstract-Microarray data processing revolves around the pivotal issue of locating genes altering their expression in response to pathogens, other organisms or other multiple environmental conditions resulted out of a comparison between infected and uninfected cells or tissues. To have a comprehensive analysis of the corollaries of certain treatments, deseases and developmental stages embodied as a data matrix on gene expression data is possible through simultaneous observation and monitoring of the expression levels of multiple genes. Clustering is the mechanism of grouping genes into clusters based on different parameters. Clustering is the process of grouping genes into clusters either considering row at a time(row clustering) or considering column at a time(column clustering). The application of clustering approach is crippled by conditions which are unrelated to genes. To get better of these problems a unique form of clustering technique has evolved which offers simultaneous clustering (both rows and columns) which is known as biclustering. A bicluster is deemed to be a sub matrix consisting data values. A bicluster is resulted out of the removal of some of the rows as well as some of the columns of given data matrix in such a fashion that each row of what is left reads the same string. A fast, simple and efficient randomized algorithm is explored in this paper, which discovers the largest bicluster by random projections.

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Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data

Cited by 5 publications

References 18 publications

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Biclustering of Gene Expression Data using a Two - Phase Method

Randomized Algorithmic Approach for Biclustering of Gene Expression Data

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