Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

cuckoo search Related Publications

2 A Cuckoo Search with Differential Evolution for Clustering Microarray Gene Expression Data

Authors: K. Premalatha, M. Pandi

Abstract:

A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.

Keywords: Genomics, Clustering, Dna, Differential Evolution, Microarray, Gene Expression Data, cuckoo search

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 964
1 Comparative Study on Swarm Intelligence Techniques for Biclustering of Microarray Gene Expression Data

Authors: K. Premalatha, A. M. Natarajan, R. Balamurugan

Abstract:

Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

Keywords: Particle Swarm Optimization, Gene Expression Data, cuckoo search, biclustering, Shuffled frog leaping

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2205