A clustering algorithm based on graph connectivity
Information Processing Letters
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Time-Series Biclustering Algorithm for Revealing Co-Regulated Genes
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Guest Editorial: Special Issue on Bioinformatics
Pattern Recognition
A linear time biclustering algorithm for time series gene expression data
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Evolutionary biclustering of microarray data
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Natural computing methods in bioinformatics: A survey
Information Fusion
A Least Squares Fitting-Based Modeling of Gene Regulatory Sub-networks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Cross-Correlation and Evolutionary Biclustering: Extracting Gene Interaction Sub-networks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Genetic Networks and Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Aggregation of correlation measures for the reverse engineering of gene regulatory sub-networks
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
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DNA Microarray experiments form a powerful tool for studying gene expression patterns, in large scale. Sharing of the regulatory mechanism among genes, in an organism, is predominantly responsible for their co-expression. Biclustering aims at finding a subset of similarly expressed genes under a subset of experimental conditions. A small number of genes participate in a cellular process of interest. Again, a gene may be simultaneously involved in a number of cellular processes. In cellular environment, genes interact among themselves to produce enzymes, metabolites, proteins, etc. responsible for a particular function(s). In this study, a simple and novel correlation-based approach is proposed to extract gene interaction networks from biclusters in microarray data. Local search strategy is employed to add (remove) relevant (irrelevant) genes for finer tuning, in multi-objective biclustering framework. Preprocessing is done to preserve strongly correlated gene interaction pairs. Experimental results on time-series gene expression data from Yeast are biologically validated using benchmark databases and literature.