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.
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- Partial requirement for: Ph.D., Arizona State University, 2011Note typethesis
- Includes bibliographical references (p. 104-111)Note typebibliography
- Field of study: Computer science