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Title: A Parallel Distributed System for Gene Expression Profiling Based on Clustering Ensemble and Distributed Optimization
Authors: Benmounah, Zakaria 
Batouche, Mohamed 
Affiliations: Faculty of Information and Communication Technology (ICT) 
Keywords: Particle Swarm Optimization;Cluster Solution;Consensus Function;Differential Evolution;Gene Expression Data
Date: 1-Dec-2013
Publisher: Springer, Cham
Related Publication(s): Algorithms and Architectures for Parallel Processing
Volume: 8285
Conference: International Conference on Algorithms and Architectures for Parallel Processing 
With the development of microarray technology, it is possible now to study and measure the expression profiles of thousands of genes simultaneously which can lead to identify subgroup of specific disease or extract hidden relationships between genes. One computational method often used to this end is clustering. In this paper, we propose a parallel distributed system for gene expression profiling (PDS-GEF) which provides a useful basis for individualized treatment of a certain disease such as Cancer. The proposed approach is based on two major techniques: the GIM (Generalized Island Model) and clustering ensemble. GIMs are used to generate good quality clusterings which are refined by a consensus function to get a high quality clustering. PDS-GEF system is implemented using Matlab®’s PCT (Parallel Computing ToolboxTM) which runs on a desktop computer, and tested on 34 different publicly available gene expression data sets. The obtained results compete with and even outperform existing methods
DOI: 10.1007/978-3-319-03859-9_14
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