Title: Comparison of gene regulatory networks using adaptive neural network and self-organising map approaches over Huh7 hepatoma cell microarray data matrix

Authors: Bandana Barman; Paramita Biswas; Anirban Mukhopadhyay

Addresses: Department of Electronics and Communication Engineering, Government Engineering College, Kalyani, WB, India ' Department of Computer Science and Technology, University of Kalyani, Kalyani, WB, India ' Department of Computer Science and Technology, University of Kalyani, Kalyani, WB, India

Abstract: Construction of gene regulatory network (GRN) is very important as it governs the expression levels of biomolecules in microarray data. In this article, we have developed GRNs by adaptive neural network (ANN) and self-organising map (SOM) approaches over Hepatitis C virus infection effect on Huh7 hepatoma cell microarray time series data. We then compared GRNs for the best performance analysis. We used fuzzy C-means clustering method to cluster the normalised dataset and then cluster centres are identified. After constructing GRNs within cluster centres, we analysed that SOM topology results a better performance providing minimum error to construct the GRN from sample data.

Keywords: gene regulatory networks; GRNs; microarray time series; gene expression data; artificial neural networks; ANNs; self-organising maps; SOM; fuzzy C-means clustering; Euclidean distance; hepatoma cell microarrays; Hepatitis C; bioinformatics.

DOI: 10.1504/IJBIC.2016.078640

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.4, pp.240 - 247

Received: 29 Nov 2014
Accepted: 05 May 2015

Published online: 30 Aug 2016 *

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