As a useful data mining technique biclustering identifies local patterns from gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. In this paper a new method is introduced based on greedy search algorithm combined with the evolutionary technique particle swarm optimization for the identification of biclusters. Greedy methods have the possibility of getting trapped in local minima. Metaheuristic methods like particle swarm optimization have features for escaping from local minima and can find global optimal solutions. In this algorithm biclusters are identified in three steps. In the first step small disjoint tightly coregulated submatrices are generated using K-Means clustering algorithm. Then greedy search algorithm is used to enlarge the seeds. Output of greedy search algorithm is used as initial population of binary PSO. The result obtained on Yeast dataset shows that this method can generate high quality biclusters.
Microarray technology demands the development of algorithms capable of extracting novel and useful patterns like biclusters. A bicluster is a submatrix of the gene expression datamatrix such that the genes show highly correlated activities across all conditions in the submatrix. A measure called Mean Squared Residue (MSR) is used to evaluate the coherence of rows and columns within the submatrix. In this paper, the KMeans greedy search hybrid algorithm is developed for finding biclusters from the gene expression data. This algorithm has two steps. In the first step, high quality bicluster seeds are generated using KMeans clustering algorithm. In the second step, these seeds are enlarged by adding more genes and conditions using the greedy strategy. Here, the objective is to find the biclusters with maximum size and the MSR value lower than a given threshold. The biclusters obtained from this algorithm on both the bench mark datasets are of high quality. The statistical significance and biological relevance of the biclusters are verified using gene ontology database.
Microarray technology demands the development of data mining algorithms for extracting useful and novel patterns. A bicluster of a gene expression dataset is a local pattern such that the genes in the bicluster exhibit similar expression patterns through a subset of conditions. In this study biclusters are detected in two steps. In the first step high quality bicluster seeds are generated using KMeans clustering algorithm. These seeds are then enlarged using a multistart metaheuristic method Greedy Randomized Adaptive Search Procedure (GRASP).In GRASP there are two phases, construction and local search. The Experimental results on the benchmark datasets demonstrate that GRASP is capable of identifying high qua;ity biclusters compared to many of the already existing biclustering algorithms. Moreover far better biclusters are obtained in this algorithm compared to the already existing algorithms based on the same GRASP metaheuristics. In this study GRASP is applied for the first time to identify biclusters from Human Lymphoma dataset.
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