Gene Regulatory Network Inference
Information Theoretic and Model Based Approaches
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Genes are the basic blue print of life in an organism and a set of genes that interact with each other to control a specific cell function is termed as Gene Regulatory Network (GRN). GRN inference is the reverse engineering approach to uncover the dynamic and intertwined nature of gene regulation in cellular systems analyzing gene expression. GRN inference is a computational intelligence task and a number of methods have been investigated those are categorized into two different approaches. In information theoretic approach, dependencies among genes are measured and then network is inferred em...
Genes are the basic blue print of life in an organism and a set of genes that interact with each other to control a specific cell function is termed as Gene Regulatory Network (GRN). GRN inference is the reverse engineering approach to uncover the dynamic and intertwined nature of gene regulation in cellular systems analyzing gene expression. GRN inference is a computational intelligence task and a number of methods have been investigated those are categorized into two different approaches. In information theoretic approach, dependencies among genes are measured and then network is inferred employing individual inference technique. In model based approach, parameter estimation of S-System model is a high dimensional optimization task. This book provides GRN inference basics and comprehensive study of major inference methods in both approaches. Several Swarm Intelligence methods are discussed and adapted to optimize S-System parameters. Prominent methods are evaluated on benchmarkgene expression datasets and are compared on the basis of standard measures. The book will be helpful for basic knowledge and research foundation on GRN inference which is a promising field of research.
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