A comparative analysis on computational methods for fitting an ERGM to biological network data

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dc.contributor.advisor Begum, Munni, 1970-
dc.contributor.author Saha, Sudipta
dc.date.accessioned 2013-05-10T15:29:26Z
dc.date.available 2013-05-10T15:29:26Z
dc.date.created 2013-05-04
dc.date.issued 2013-05-04
dc.identifier.uri http://cardinalscholar.bsu.edu/handle/123456789/197173
dc.description.abstract Understanding of a global biological network structure by studying its simple local properties through the well-developed field of graph theory is of interest. In particular, in this research an observed biological network was explored through a simulation study. However, one difficulty in such exploration lies on the fitting of graphical models on biological network data. An Exponential Random Graph Model (ERGM) was considered to determine estimations of the several network attributes of complex biological network data. We also compared the estimates of observed network to our random simulated network for both Markov Chain Monte Carlo Maximum Likelihood Estimation (MCMCMLE) and Maximum Pseudo Likelihood Estimation (MPLE) methods under ERGM. The motivation behind this was to determine how different the observed network could be from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of E. coli.
dc.description.sponsorship Department of Mathematical Sciences
dc.subject.lcsh Biological systems -- Mathematical models
dc.subject.lcsh Graph theory
dc.subject.lcsh Network analysis (Planning)
dc.subject.lcsh Escherichia coli -- Genetics -- Mathematical models
dc.title A comparative analysis on computational methods for fitting an ERGM to biological network data en_US
dc.description.degree Thesis (M.S.)
dc.identifier.cardcat-url http://liblink.bsu.edu/catkey/1712474


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  • Master's Theses [5318]
    Master's theses submitted to the Graduate School by Ball State University master's degree candidates in partial fulfillment of degree requirements.

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