ERA

Download the full-sized PDF of The Budgeted Biomarker Discovery ProblemDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3QF8JQ6T

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

The Budgeted Biomarker Discovery Problem Open Access

Descriptions

Other title
Subject/Keyword
machine learning
association studies
bioinformatics
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Khan, Sheehan Veikko
Supervisor and department
Greiner, Russell (Computing Science)
Examining committee member and department
Tiwari, Hemant (Biostatistics)
Zaiane, Osmar (Computing Science)
Greiner, Russell (Computing Science)
Baracos, Vickie (Oncology)
Wishart, David (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-05-29T15:10:09Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Researchers conduct association studies to discover biomarkers in order to gain new biological insight on complex diseases and phenotypes. Although most researchers have intuitions about what defines a biomarker and how to assess the results of an association study, there is neither a formal definition for what a biomarker is, nor objective goal for association studies. As a result, the literature is full of association studies with conflicting results – e.g., studies on the same phenotype that produce lists of biomarkers with little to no overlap. This thesis presents the “Budgeted Biomarker Discovery (BBD) problem”, which clearly defines (1) what a biomarker is, and (2) rewards for correctly identifying biomarkers and penalties for incorrectly identifying biomarkers. Furthermore, the BBD problem allows researchers to use a mixture of high- and low-throughput technologies. In the context of discovering biomarkers from gene expression data, we show how future association studies can use both microarrays and qPCR data to objectively find the genes that are biomarkers in a cost efficient manner. We present several algorithms for solving the BBD problem, and show that good algorithms must make use of both microarrays and qPCR. Also, they must be able to adapt to the data as it is collected. For example, when solving a new BBD problem, we must begin by collecting microarrays because we do not yet know how many biomarkers we expect to identify, or which qPCR arrays would be most informative. Thus, we use the high-throughput microarrays to survey the problem, until we can identify which specific low-throughput qPCR arrays to use for focusing on those genes that are potentially biomarkers. To identify when this transition should occur, we present the problem of estimating the density of univariate statistics in high-throughput data, and we present our Fused Density Estimation (FDE) algorithm as a solution. We use FDE as the backbone of our adaptive algorithms for solving BBD problems. In a series of experiments on real microarray data and realistic synthetic data, we show that our BBD1 algorithm is the most robust solution, amongst those considered, to the BBD problem.
Language
English
DOI
doi:10.7939/R3QF8JQ6T
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2015-05-29T21:10:10.790+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (PDF/A)
Mime type: application/pdf
File size: 23437574
Last modified: 2016:06:24 18:20:13-06:00
Filename: Khan_Sheehan_V_201505_PhD.pdf
Original checksum: 357ba3bfb9bf9b524db74da3696918e7
Activity of users you follow
User Activity Date