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Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks

Abstract As we migrate into an era of personalized medicine, understanding how bio-molecules interact with one another to form cellular systems is one of the key focus areas of systems biology. Several challenges such as the dynamic nature of cellular systems, uncertainty due to environmental influences, and the heterogeneity between individual patients render this a difficult task. In the last decade, several algorithms have been proposed to elucidate cellular systems from data, resulting in numerous data-driven hypotheses. However, due to the large number of variables involved in the process, many of which are unknown or not measurable, such computational approaches often lead to a high proportion of false positives. This renders interpretation of... (more)
Created Date 2012
Contributor Ramesh, Archana (Author) / Kim, Seungchan (Advisor) / Langley, Patrick W (Committee member) / Baral, Chitta (Committee member) / Kiefer, Jeffrey (Committee member) / Arizona State University (Publisher)
Subject Computer science / Bioinformatics / Artificial intelligence / data mining / gene regulatory networks / knowledge integration / machine learning / microarray data
Type Doctoral Dissertation
Extent 130 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis