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A Computational Framework to Model and Learn Context-Specific Gene Regulatory Networks from Multi-Source Data


Abstract Reverse engineering gene regulatory networks (GRNs) is an important problem in the domain of Systems Biology. Learning GRNs is challenging due to the inherent complexity of the real regulatory networks and the heterogeneity of samples in available biomedical data. Real world biological data are commonly collected from broad surveys (profiling studies) and aggregate highly heterogeneous biological samples. Popular methods to learn GRNs simplistically assume a single universal regulatory network corresponding to available data. They neglect regulatory network adaptation due to change in underlying conditions and cellular phenotype or both. This dissertation presents a novel computational framework to learn common regulatory interactions an... (more)
Created Date 2011
Contributor Sen, Ina (Author) / Kim, Seungchan (Advisor) / Baral, Chitta (Committee member) / Bittner, Michael (Committee member) / Konjevod, Goran (Committee member) / Arizona State University (Publisher)
Subject Computer Science
Type Doctoral Dissertation
Extent 135 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis