Physical Sciences and Mathematics Commons^™

Microfluidic Devices With Integrated Sample Preparation For Improved Analysis Of Protein Biomarkers, Pamela Nsang Nge

Theses and Dissertations

Biomarkers present a non-invasive means of detecting cancer because they can be obtained from body fluids. They can also be used for prognosis and assessing response to treatment. To limit interferences it is essential to pretreat biological samples before analysis. Sample preparation methods include extraction of analyte from an unsuitable matrix, purification, concentration or dilution and labeling. The many advantages offered by microfluidics include portability, speed, automation and integration. Because of the difficulties encountered in integrating this step in microfluidic devices most sample preparation methods are often carried out off-chip. In the fabrication of micro-total analysis systems it is important …

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 …