Dissertation Defense Announcement
To: The George Mason University Community
Candidate: Tiange Cui
Program: PhD in Bioinformatics & Computational Biology
Date: Tuesday August 28, 2018
Time: 1:00 PM
Place: George Mason University
Science & Tech campus
Colgan Hall, Room 328-H
Title: "PreDist: Distance-based Metrics as Potential Diagnostic and Prediction Classifiers for Human Diseases"
Committee Chair: Dr. Ancha Baranova
Committee Members: Dr. Donald Seto, Dr. Patrick Gillevet, Dr. Boris Veytsman
This is a public defense and all are invited to attend.
The emergence of next-generation sequencing (NGS) and microarray technologies has greatly changed today's approach to cancer research. The high sensitivity and specificity of NGS and microarray techniques significantly facilitate modern research as they enable the collection of the whole genome, transcriptome and proteome data instrumental for evolutionary, functional and translational inference. In addition, omics profiling assists clinical scientists in identification of clinically and genetically different disease subgroups and progressive states. By comparing the omics profiles as a whole rather than selecting a list of top-ranked biomarkers, we may achieve more accurate diagnostic and prognostic results. Furthermore, using these holistic measures may aid in identifying biological outliers, and, therefore, in proper categorization of studied specimens. Lastly, holistic omics solutions may alleviate a critical shortcoming of the widely used TNM cancer staging system, which is limited by anatomic information, and does not meet current needs in the era of biomarker-driven and personalized medicine. In this dissertation, I aim to address these issues by applying a novel distance-based metric, PreDist, to the study of biological specimens which correspond to various pathophysiological states. Here I demonstrate that expression-based distances are capable of bridging omics datasets and discuss practical applications of this metric for supplementing existing TNM staging system and discriminating the severity of human diseases. Using Pearson's distances, we demonstrated the omics profiling can be used to quantitatively describe regulatory landscapes in normal and diseased human tissues. We show that diagnostic and prediction performance of microRNA(miRNA) profile is superior to that of mRNA profiling. PreDist and all supplementary tools are freely available for anyone who is interested to explore the rapidly-growing omics data mines on request.