Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
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)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Iterated local search for biclustering of microarray data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
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A bicluster in gene expression dataset is a subset of genes that exhibit similar expression patterns through a subset of conditions. In this work biclusters are identified in two steps. In the first step high quality bicluster seeds are generated using KMeans clustering algorithm. These seeds are then enlarged using Reactive Greedy Randomized Adaptive Search Procedure (RGRASP) which is a multi-start metaheuristic method in which there are two phases, construction and local search. The objective here is to identify biclusters of maximum size with MSR lower than a given threshold. Experiments are conducted on both Yeast and Human Lymphoma datasets. The Experimental results on the benchmark datasets demonstrate that RGRASP is capable of identifying high quality biclusters compared to many of the already existing biclustering algorithms. Compared to the already existing algorithm based on the same RGRASP metaheuristics biclusters with larger size and lower mean squared residue are obtained using this algorithm in Yeast dataset. Moreover in this study the RGRASP is applied for the first time to find biclusters from the Human Lymphoma dataset.