Iterated Local Search for Biclustering of Microarray Data

Ayadi, Wassim; Elloumi, Mourad; Hao, Jin‐Kao

doi:10.1007/978-3-642-16001-1_19

Cited by 15 publications

(17 citation statements)

References 27 publications

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“…The other consists of not only TOP 1 but also TOP 2 and 3 columns. Bimax [11], BiBit [12], PDNS [8], BISES [5] and BISERS are applied to the former matrix, which requires no exclusive selection of a column. BISES and BISERS are applied to the latter matrix.…”

Section: Comparison With Other Biclustering Methodsmentioning

confidence: 99%

“…In PDNS algorithm [8], the bicluster B(I, J) (I is a set of rows and J is a set of columns included in the bicluster) is evaluated by Average Spearman's Rho (ASR) defined as follows. ),…”

Section: Evaluation Functionmentioning

confidence: 99%

“…BISES, a previous version of biclustering algorithm we have proposed, is based on the algorithm PDNS (Pattern-Driven Neighborhood Search) proposed by Ayadi et al [8], in which biclusters are evaluated by correlations of rows and columns and are incrementally updated, namely adding or removing rows and columns, by neighborhood search. BISES introduces the inter-column similarity and the exclusive selection from 'group of columns' in order to deal with the first and the last points mentioned above, but does not consider second point.…”

mentioning

confidence: 99%

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A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces

Nishimura¹,

Ohkawa²

2018

IJBBB

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Abstract:A protein and a ligand have a process of recognizing each other when they are remote before approaching for binding. In this process, there should be a local portion corresponding to a target of the recognition (a recognition spot) on the protein molecular surface. In this paper, we proposed a new biclustering algorithm BISERS for predicting recognition spots on protein molecular surfaces, in which a portion of the molecular surface of a query protein that frequently shows the similarity to other specific proteins binding to the similar ligand is extracted by biclustering. In BISERS, the similarity between rows as well as the similarity between columns is introduced into the evaluation function for updating the biclusters. In addition, the random sampling is applied to the process of exclusive selection of the column for reducing the computational cost effectively. Experimental results for 60 proteins show the effectiveness of our BISERS algorithm.

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Section: Comparison With Other Biclustering Methodsmentioning

confidence: 99%

“…In PDNS algorithm [8], the bicluster B(I, J) (I is a set of rows and J is a set of columns included in the bicluster) is evaluated by Average Spearman's Rho (ASR) defined as follows. ),…”

Section: Evaluation Functionmentioning

confidence: 99%

mentioning

confidence: 99%

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A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces

Nishimura¹,

Ohkawa²

2018

IJBBB

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“…In the construction phase, one implements a k-means and a second one a greedy randomized procedure inspired by a minimum spanning tree of a suitable weighted graph. Then, two types of local searches have been implemented: one has been already proposed [3] and a second one is an Iterated Local Search [4]. All the designed algorithms have been tested and compared using the lymphoma dataset [5].…”

Section: Methodsmentioning

confidence: 99%

New metaheuristics approaches for biclustering of gene expression Data

et al. 2012

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MotivationsBiclustering or simultaneous clustering of both genes and conditions have generated considerable interest over the past few decades, particularly related to the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining [1]. Given a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Therefore, given the inner "intractability" of the problem from a computational point of view, to efficiently find good solutions in a reasonable running times we have designed and implemented several different metaheuristics based on a GRASP framework.

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“…In this paper, we design an algorithm that discovers linear coherent bi-clusters that are arbitrarily positioned and possibly even overlapping [16]. Note that, although bi-clusters cannot be simultaneously row-wise and column-wise linear coherent, one is usually more interested in clustering one dimension than the other [9,2]. For example, in the case of gene expression analysis, the main purpose is to identify groups of genes that co-participate in certain genetic regulatory process, hence grouping conditions (samples) is only a secondary consideration.…”

Section: Introductionmentioning

confidence: 99%

Sparse Learning Based Linear Coherent Bi-clustering

Shi

Liao

Zhang

et al. 2012

Lecture Notes in Computer Science

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Abstract. Clustering algorithms are often limited by an assumption that each data point belongs to a single class, and furthermore that all features of a data point are relevant to class determination. Such assumptions are inappropriate in applications such as gene clustering, where, given expression profile data, genes may exhibit similar behaviors only under some, but not all conditions, and genes may participate in more than one functional process and hence belong to multiple groups. Identifying genes that have similar expression patterns in a common subset of conditions is a central problem in gene expression microarray analysis. To overcome the limitations of standard clustering methods for this purpose, Bi-clustering has often been proposed as an alternative approach, where one seeks groups of observations that exhibit similar patterns over a subset of the features. In this paper, we propose a new bi-clustering algorithm for identifying linear-coherent bi-clusters in gene expression data, strictly generalizing the type of bi-cluster structure considered by other methods. Our algorithm is based on recent sparse learning techniques that have gained significant attention in the machine learning research community. In this work, we propose a novel sparse learning based model, SLLB, for solving the linear coherent bi-clustering problem. Experiments on both synthetic data and real gene expression data demonstrate the model is significantly more effective than current biclustering algorithms for these problems. The parameter selection problem and the model's usefulness in other machine learning clustering applications are also discussed. The Appendix of this paper can be found on http://www.cs.ualberta.ca/~ys3/SLLB.

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Iterated Local Search for Biclustering of Microarray Data

Cited by 15 publications

References 27 publications

A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces

A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces

New metaheuristics approaches for biclustering of gene expression Data

Sparse Learning Based Linear Coherent Bi-clustering

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