Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12202/4488
Title: Topological Approaches to Understanding Genetic Networks.
Authors: Gidea, Marian
Liebling, Marjorie C.
Keywords: senior honors thesis
Topological approaches
genetic networks
Issue Date: 7-May-2019
Publisher: Stern College for Women. Yeshiva University.
Citation: Liebling, Marjorie C. Topological Approaches to Understanding Genetic Networks Presented to the S. Daniel Abraham Honors Program in Partial Fulfillment of the Requirements for Completion of the Program. Ma7 7, 2019.
Abstract: In this study, we present methods of network analysis from both statistical and topological points of view to enhance our understanding of the robustness of the networks and the gene – gene interactions they exhibit. Using different strategies, networks were learned from a single cell dataset in which expression values were collected during various stages of cell development. The bnlearn algorithm was used to construct directed and acyclic Bayesian networks for statistical analysis which revealed greater homogeneity of activating interactions than that of inhibiting interactions. Because topological data analysis relies of cycles formed by edges in the network which are – by construction – not present in the Bayesian networks, new networks were learned by computing entropy, mutual information, and distances manually. Performance of persistence homology with the TDA algorithm revealed topological features which rendered the networks topologically distinct.
Description: The file is restricted for YU community access only.
URI: https://hdl.handle.net/20.500.12202/4488
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Appears in Collections:S. Daniel Abraham Honors Student Theses

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