Dept. of Medical Informatics and Clinical Epidemiology
Oregon Health & Science University
This work develops the methods necessary for deriving integrated network signatures of disease with an application in systems biology of infectious disease. Co-expression network models help identify important biochemical pathways, biomarkers and targets for research, but they typically focus on gene expression. In this work, co-expression network methodologies are extended to proteomics and applied to data derived from mice infected with either influenza or SARS-CoV. Although, peptide-level co-expression networks are promising, the determination of parent proteins is difficult, especially due to degenerate peptides mapping to multiple proteins. Protein inference attempts to solve this problem. While there are a handful of models, none have been purposed towards high-throughput tag-based proteomics. As such, a new model for protein inference is developed, representing a new approach to the problem. Lastly, two methods of data integration are explored. First, using a new application
School of Medicine
Gibbs, David L., "Integrated signatures of disease using network methods" (2012). Scholar Archive. 879.