Identification of optimal parameter ranges in building and assessing correlation networks built from gene expression.

In this project, I investigate and define the range of acceptable outputs of a gene expression correlation network model. Gene expression refers to the amount of product made by a gene under a given biological condition. A correlation network is a graphical model where the nodes represent genes in an organism and the edges represent the amount of correlation between genes, based on their expression. Correlation network modeling has been used in cellular and biomedical domains to identify functional relationships between genes. The network model in general is a wonderful tool for showcasing relationships, but often times they are misused due to lack of standards specifications in this domain. In this project, I am using correlation networks to show the interaction between multiple datasets. Specifically, through analysis the parameters of networks, I discover the potential and acceptable output range of each parameter by measuring structures in the network. These ranges can guide future research as a reference benchmark for researchers who desire to use this model in an effective and reproducible way