Gene regulatory network inference: Data integration in dynamic models - A review

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ScholarWorks @ UTRGV ScholarWorks @ UTRGV (background): “In doing so, the analysis of SNP-gene regulatory networks (SGRN) will provide more definite relationships of genotypic causes and phenotypic effects so that it will facilitate prognosis and drug designs for therapies.”
Inference of complex biological networks: distinguishability issues and optimization-based solutions (2011, background): “gene expression [5-13], signal transduction [14-17] and metabolic networks [18-25].”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “curacy of the resulting predictions [13, 147].”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “The gene regulatory network (GRN) is a complex system associated with biological activities at the cellular level, such as cell growth, division, development, and response to environmental stimulus (Carthew and Sontheimer 2009), which should be modeled in a dynamic way (Hecker et al. 2009).”