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

作者：Barman Bandana^*; Biswas Paramita; Mukhopadhyay Anirban

来源：International Journal of Bio-Inspired Computation, 2016, 8(4): 240-247.

DOI：10.1504/IJBIC.2016.10000265

• gene regulatory network
• GRN
• microarray time series gene expression data
• adaptive neural network
• ANN
• self-organisation map
• SOM
• fuzzy C-means clustering
• Euclidean distance

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.

• 出版日期2016