Recovery in a shared disk parallel database system using DFeRAM
HPC-ASIA '97 Proceedings of the High-Performance Computing on the Information Superhighway, HPC-Asia '97
Biclustering of Gene Expression Data by Simulated Annealing
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Stability and Performances in Biclustering Algorithms
Computational Intelligence Methods for Bioinformatics and Biostatistics
Application of reactive GRASP to the biclustering of gene expression data
ISB '10 Proceedings of the International Symposium on Biocomputing
Biclustering gene expression data using KMeans-binary PSO hybrid
ISB '10 Proceedings of the International Symposium on Biocomputing
A novel approach for biclustering gene expression data using modular singular value decomposition
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
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
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data
Pattern Recognition Letters
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A bicluster of a gene expression dataset captures thecoherence of a subset of genes and a subset of conditions.Biclustering algorithms are used to discoverbiclusters whose subset of genes are co-regulated undersubset of conditions. In this paper, we present anovel approach, called DBF (Deterministic Biclusteringwith Frequent pattern mining) to finding biclusters.Our scheme comprises two phases. In the first phase, wegenerate a set of good quality biclusters based on frequentpattern mining. In the second phase, the biclustersare further iteratively refined (enlarged) by addingmore genes and/or conditions. We evaluated our schemeagainst FLOC and our results show that DBF can generatelarger and better biclusters.