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    A novel machine learning framework for phenotype prediction based on Geome-wide DNA methylation data

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    t16074_Karagod.pdf (899.8Kb)
    Date
    2016-12
    Author
    Karagod, Vinay Vittal
    Advisor
    Sinha, Kaushik
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    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.
    Description
    Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
    URI
    http://hdl.handle.net/10057/13482
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