A novel approach for biclustering gene expression data using modular singular value decomposition

Authors:
V. N. Manjunath Aradhya;Francesco Masulli;Stefano Rovetta
Affiliations:
Dept. of Computer and Information Sciences, University of Genova, Genova, Italy and Dept. of ISE, Dayananda Sagar College of Engg., Bangalore, India;Dept. of Computer and Information Sciences, University of Genova, Genova, Italy and Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Temple University, Philad ...;Dept. of Computer and Information Sciences, University of Genova, Genova, Italy
Venue:
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Year:
2009

Citing 20
Cited 2

Data clustering: a review

ACM Computing Surveys (CSUR)
Co-clustering documents and words using bipartite spectral graph partitioning

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering local structure in gene expression data: the order-preserving submatrix problem

Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis

BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Enhanced Biclustering on Expression Data

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Information-theoretic co-clustering

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Deterministic Biclusters in Gene Expression Data

BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Defining transcription modules using large-scale gene expression data

Bioinformatics
A partitioning based algorithm to fuzzy co-cluster documents and words

Pattern Recognition Letters
BicAT: a biclustering analysis toolbox

Bioinformatics
A systematic comparison and evaluation of biclustering methods for gene expression data

Bioinformatics
Computing the maximum similarity bi-clusters of gene expression data

Bioinformatics
Multi-objective evolutionary biclustering of gene expression data

Pattern Recognition
Possibilistic approach for biclustering microarray data

Computers in Biology and Medicine
Towards full automation of lexicon construction

CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis

CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Noise-robust algorithm for identifying functionally associated biclusters from gene expression data

Information Sciences: an International Journal
Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Recently, biclustering (or co-clustering), performing simultaneous clustering on the row and column dimensions of the data matrix, has been shown to be remarkably effective in a variety of applications. In this paper we propose a novel approach to biclustering gene expression data based on Modular Singular Value Decomposition (Mod-SVD). Instead of applying SVD directly on on data matrix, the proposed approach computes SVD on modular fashion. Experiments conducted on synthetic and real dataset demonstrated the effectiveness of the algorithm in gene expression data.