Life Sciences Commons^™

Multifactorial Patterns Of Gene Expression In Colonic Epithelial Cells Predict Disease Phenotypes In Experimental Colitis, Aubrey Leigh Frantz, Maria E. C. Bruno, Eric William Rogier, Halide Tuna, Donald A. Cohen, Subbarao Bondada, Ralph Lakshman Chelvarajan, J. Anthony Brandon, C. Darrell Jennings, Charlotte S. Kaetzel

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Background— The pathogenesis of inflammatory bowel disease (IBD) is complex and the need to identify molecular biomarkers is critical. Epithelial cells play a central role in maintaining intestinal homeostasis. We previously identified five “signature” biomarkers in colonic epithelial cells (CEC) that are predictive of disease phenotype in Crohn's disease. Here we investigate the ability of CEC biomarkers to define the mechanism and severity of intestinal inflammation.

Methods— We analyzed the expression of RelA, A20, pIgR, tumor necrosis factor (TNF), and macrophage inflammatory protein (MIP)-2 in CEC of mice with dextran sodium sulfate (DSS) acute colitis or T-cell-mediated chronic colitis. …

Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris

Jeffrey S. Morris

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …