Abstract
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.
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Benmounah, Z., Batouche, M. (2013). A Parallel Distributed System for Gene Expression Profiling Based on Clustering Ensemble and Distributed Optimization. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8285. Springer, Cham. https://doi.org/10.1007/978-3-319-03859-9_14
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DOI: https://doi.org/10.1007/978-3-319-03859-9_14
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