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Synchronized Clustering: A Review On Systematic Comparisons And Validation Of Prominent Block-Clustering Algorithms

Dr.Mani Sarma Vittapu
2017 Zenodo  
The main goal of this paper is to provide a systematic comparison and validation of prominent bi-clustering algorithms.  ...  Abstract: One of the major problems in clustering is the need of specifying the optimal number of clusters in some clustering algorithms.  ...  Fig. 3 . a) Effect of overlapping in the algorithm and the biclusters. 1) Best SS measure achieved by using F1 statistics along with the mean of SS for all the proven configurations. 2) Variation in the  ... 
doi:10.5281/zenodo.821158 fatcat:ys4cxx6cyfbxjdl4phif452kfi

Biclustering Models for Structured Microarray Data

H.L. Turner, T.C. Bailey, W.J. Krzanowski, C.A. Hemingway
2005 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data.  ...  We describe how the extended models mav be fitted and illustrate their use on real data.  ...  The algorithm finds a bicluster of genes and samples for which the layer model fits better than the null model.  ... 
doi:10.1109/tcbb.2005.49 pmid:17044169 fatcat:sawbgjf52nhalnmhzvm626wfte

A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application

Shoulin Yin, Jie Liu, Lin Teng
2016 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
In order to further improve the performance of biclustering algorithm, this paper proposes a semisupervised clustering algorithm based on variational Bayesian.  ...  Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model.  ...  and corresponds to a lower bound for the Bayesian evidence, is a key quantity for model selection.  ... 
doi:10.12928/telkomnika.v14i3.3805 fatcat:nbg252n7mbey5lvlunhezq2o2m

Configurable pattern-based evolutionary biclustering of gene expression data

Beatriz Pontes, Raúl Giráldez, Jesús S Aguilar-Ruiz
2013 Algorithms for Molecular Biology  
Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions.  ...  Results: Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives.  ...  The algorithm takes as input paramater the number of partial models passed for each iteration .  ... 
doi:10.1186/1748-7188-8-4 pmid:23433178 pmcid:PMC3668234 fatcat:mxgjhqyisbby3b5usbgx5qsgaq

Finding large average submatrices in high dimensional data

Andrey A. Shabalin, Victor J. Weigman, Charles M. Perou, Andrew B. Nobel
2009 Annals of Applied Statistics  
Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.)  ...  The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data.  ...  Biclusters identified by the CC algorithm have the smallest average variance, as CC searches for biclusters with low unexplained variation.  ... 
doi:10.1214/09-aoas239 fatcat:mnznvgq3ozdx7kf65ascv7yidi

FABIA: factor analysis for bicluster acquisition

Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, Suzy Van Sanden, Dan Lin, Willem Talloen, Luc Bijnens, Hinrich W. H. Göhlmann (+2 others)
2010 Computer applications in the biosciences : CABIOS  
On these data sets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content.  ...  Results: On 100 simulated data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors.  ...  Section 2 introduces the multiplicative bicluster model class. Section 3 describes the model selection (training) algorithm for the new model class.  ... 
doi:10.1093/bioinformatics/btq227 pmid:20418340 pmcid:PMC2881408 fatcat:qcsflwk6mbd4tb5zr5aulyohii

On Variational Inference in Biclustering Models

Guanhua Fang, Ping Li
2021 International Conference on Machine Learning  
In this paper, we develop a theory for the estimation of general biclustering models, where the data is assumed to follow certain statistical distribution with underlying biclustering structure.  ...  Furthermore, we study the convergence property of the coordinate ascent variational inference algorithm, where both local and global convergence results have been provided.  ...  Acknowledgement The authors sincerely thank the anonymous reviewers and area chair for their constructive comments.  ... 
dblp:conf/icml/FangL21a fatcat:bxiwl6rtnffavcn7lgaqffpuwy

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks [article]

Yunpeng Zhao, Ning Hao, Ji Zhu
2022 arXiv   pre-print
The latent block model (LBM) has been proposed as a model-based tool for biclustering.  ...  We develop an efficient variational expectation-maximization algorithm by observing that the row and column degrees maximize the objective function in the M step given any probability assignment on the  ...  Although developed almost independently with biclustering, the models and algorithms for community detection, and challenges that they face are similar to biclustering.  ... 
arXiv:2206.08465v1 fatcat:xr5w5gz55bamleaayqfhzazsbi

Sparse Biclustering of Transposable Data

Kean Ming Tan, Daniela M. Witten
2014 Journal of Computational And Graphical Statistics  
In addition, we propose a framework for biclustering based on the matrix-variate normal distribution.  ...  We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood.  ...  Acknowledgments We thank the editor, an associate editor, and two reviewers for helpful comments that improved the quality of this manuscript.  ... 
doi:10.1080/10618600.2013.852554 pmid:25364221 pmcid:PMC4212513 fatcat:d4qhlsdaznh2ddndqxd3lgjdbq

Biclustering Algorithms Based on Metaheuristics: A Review [article]

Adan Jose-Garcia, Julie Jacques, Vincent Sobanski, Clarisse Dhaenens
2022 arXiv   pre-print
The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators.  ...  Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics.  ...  Year Ruiz [19] presented the sequential multi-objective biclustering (SMOB) algorithm for finding biclusters of high quality with large variation.  ... 
arXiv:2203.16241v1 fatcat:27dbyqnrnnfy5esctvbyho2hom


Ons Maatouk, Emna Ayari, Hend Bouziri, Wassim Ayadi
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association.  ...  These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP).  ...  It is based on a sub-matrix search process corresponding to a subset of rows presenting a consistent model for a certain subset of columns of the data matrix.  ... 
doi:10.1145/3520304.3528802 fatcat:g3kfonwtpfemdd5o3i45tlbili

Biclustering on expression data: A review

Beatriz Pontes, Raúl Giráldez, Jesús S. Aguilar-Ruiz
2015 Journal of Biomedical Informatics  
based on evaluation measures and non metric-based biclustering algorithms.  ...  Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters.  ...  For a single bicluster, the same model as in the plaid model [58] is assumed.  ... 
doi:10.1016/j.jbi.2015.06.028 pmid:26160444 fatcat:3w5p45w4zzebxmnphdue67ueny

An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification

Shazlyn Milleana Shaharudin, Shuhaida Ismail, Siti Mariana Che Mat Nor, Norhaiza Ahmad
2019 IAES International Journal of Artificial Intelligence (IJ-AI)  
Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation  ...  The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA.  ...  ACKNOWLEDGEMENTS The authors wish to express gratitude towards Universiti Pendidikan Sultan Idris for their support and financial funding via GPU grant Vote No. 2018-0154-101-01.  ... 
doi:10.11591/ijai.v8.i3.pp237-243 fatcat:ixh2db3hpbbf5gp4ejlrrgfuhe

A comparative analysis of biclustering algorithms for gene expression data

K. Eren, M. Deveci, O. Kucuktunc, U. V. Catalyurek
2012 Briefings in Bioinformatics  
However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms.  ...  The algorithms were tested on a suite of synthetic datasets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters  ...  Two factor analysis models are used to fit this model to the data set; variational expectation maximization is used to maximize the posterior.  ... 
doi:10.1093/bib/bbs032 pmid:22772837 pmcid:PMC3659300 fatcat:r3xctzpbbvayfipgq65jdotu74

Optimisation algorithms for microarray biclustering

Dimitri Perrin, Christophe Duhamel
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Given a robust weighting scheme proposed in [7] , we modelled the problem as a biclustering problem for which we proposed a propagation heuristic and several variations of a genetic algorithm.  ...  [15] for a general review), and a number are variations of the concept of clustering.  ... 
doi:10.1109/embc.2013.6609569 pmid:24109756 dblp:conf/embc/PerrinD13 fatcat:5a5qon4y4rg63oimkd7qnmlt3e
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