Loading...
A novel machine learning framework for phenotype prediction based on Geome-wide DNA methylation data
Karagod, Vinay Vittal
Karagod, Vinay Vittal
Citations
Altmetric:
Files
Loading...
t16074_Karagod.pdf
Adobe PDF, 899.83 KB
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2016-12
Type
Thesis
Genre
Keywords
Subjects (LCSH)
Electronic thesis
Electronic dissertations
Electronic dissertations
Citation
Abstract
DNA methylation (DNAm) is an epigenetic mechanism used by cells to control gene expression, and identification of DNAm biomarkers can assist in early diagnosis of cancer. Identification of these biomarkers can be done using CpG (Cytosine-phosphate guanine) sites, or particular regions in DNA. Previous machine learning methods known as MS-SPCA and EVORA have been used to link DNAm biomarkers to specific stages of cervical cancer using CpG data. In this work, it is shown that a proposed framework yields greater AUC accuracy than the MS-SPCA and EVORA for predicting stages of cervical cancer using CpG data. This framework appears promising in regards to the data examined herein as well as in future biological studies.
Table of Contents
Description
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
Publisher
Wichita State University
Journal
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Copyright 2016 by Vinay Karagod