Title:
Advancing precision medicine through integrative bioinformatics approaches for robust biological knowledge discovery

Thumbnail Image
Author(s)
Wu, Po-Yen Leo
Authors
Advisor(s)
Wang, May Dongmei
Advisor(s)
Editor(s)
Associated Organization(s)
Series
Supplementary to
Abstract
Facilitated by -omic data, precision medicine is a promising medical model that may revolutionize the quality of the current healthcare system. Currently, -omic data are being rapidly accumulated because of the advent of high-throughput -omic assays. Though challenging, abundant information embedded in these data is encouraging for the realization of precision medicine. Data analytics, including data pre-processing and data modeling techniques, has been successfully applied to many -omic applications, and biomarkers identified from -omic data are viewed as catalyzers for precision medicine. The goal of my Ph.D. research is to address some key challenges in the process from raw -omic data to disease subgroup assignment for precision medicine, including (1) the lack of standardized bioinformatics pipelines that extract high-quality gene expression from the raw RNA sequencing data; (2) the lack of quantitative assessment of the contribution of upstream pipeline components to downstream variations in identified biomarkers and clinical endpoint prediction performance; and (3) the lack of effective strategies for integrating knowledge derived from multiple -omic data sources, either the same type or different types. This dissertation addresses these challenges through three specific aims: (1) Quality Control for Precision Medicine: to investigate and control the impact of bioinformatics pipelines on feature quality using RNA sequencing data. (2) Knowledge Discovery for Precision Medicine: to discover impactful biomarkers that facilitate disease subgroup classification using NGS data (3) Integrative Analysis for Precision Medicine: to integrate multi-source, multi-modal -omic data for improved disease subgroup classification. The research in this dissertation was completed in frequent collaboration with the Food and Drug Administration, Children’s Healthcare of Atlanta, Emory University, and Georgia Institute of Technology. Proposed analytical approaches for NGS data have been systematically evaluated and validated using a variety of experimental designs with various NGS datasets. These results and associated case studies demonstrate the contribution of this work to and its future potential in the paradigm shift from current pattern-based, evidence-based medicine to future algorithm-based precision medicine.
Sponsor
Date Issued
2017-01-27
Extent
Resource Type
Text
Resource Subtype
Dissertation
Rights Statement
Rights URI