Regular ArticleMultivariate Measurement of Gene Expression Relationships
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2016, Computational Biology and ChemistryCitation Excerpt :The ability of neural network to learn from the data, estimate any multivariate nonlinear function and its noise tolerant capability makes ANN fit for modeling gene regulatory interactions using gene expression profiles. Several variants of ANNs have been deployed for modeling GRNs, including multilayer perceptrons (Kim et al., 2000; Huang et al., 2003; Zhou et al., 2004), self-organizing feature maps (SOFM) (Weaver et al., 1999) and recurrent neural networks (RNNs) (Vohradsky, 2001; Keedwell Ed Narayanan and Savic, 2002; Huang et al., 2003; Zhou et al., 2004; Hu et al., 2005; Xu et al., 2007a, 2007b; Chiang and Chao, 2007; Datta et al., 2009; Zhang et al., 2009; Maraziotis et al., 2010; Ghazikhani et al., 2011; Liu et al., 2011; Kentzoglanakis, 2012; Noman et al., 2013). Vohradsky, 2001 proposed an ANN based model of gene regulation assuming that the regulation effect on gene expression of a particular gene can be expressed in the form of ANN.
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