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Handbook of Dynamic Data Driven Applications Systems pp 233–252Cite as

Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation

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Abstract

The term ‘epigenetic’ refers to all heritable alterations that occur in a given gene function without having any change on the DeoxyriboNucleic Acid (DNA) sequence. Epigenetic modifications play a crucial role in development and differentiation of various diseases including cancer. The specific epigenetic alteration that has garnered a great deal of attention is DNA methylation, i.e., the addition of a methyl-group to cytosine. Recent studies have shown that different tumor types have distinct methylation profiles. Identifying idiosyncratic DNA methylation profiles of different tumor types and subtypes can provide invaluable insights for accurate diagnosis, early detection, and tailoring of the related treatment for cancer. In this study, our goal is to identify the informative genes (biomarkers) whose methylation level change correlates with a specific cancer type or subtype. To achieve this goal, we propose a novel high dimensional learning framework inspired by the dynamic data driven application systems paradigm to identify the biomarkers, determine the outlier(s) and improve the quality of the resultant disease detection. The proposed framework starts with a principal component analysis (PCA) followed by hierarchical clustering (HCL) of observations and determination of informative genes based on the HCL predictions. The capabilities and performance of the proposed framework are demonstrated using a DNA methylation dataset stored in Gene Expression Omnibus (GEO) DataSets on lung cancer. The preliminary results demonstrate that our framework outperforms the conventional clustering algorithms with embedded dimension reduction methods, in its efficiency to identify informative genes and outliers, and removal of their contaminating effects at the expense of reasonable computational cost.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Miami, Coral Gables, FL, USA

    Haluk Damgacioglu & Nurcin Celik

  2. Mechanical and Aerospace Engineering Department, University of Miami, Coral Gables, FL, USA

    Emrah Celik

  3. Chemical Engineering Department, Purdue University, West Lafayette, IN, USA

    Chongli Yuan

Authors
  1. Haluk Damgacioglu
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  2. Emrah Celik
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  3. Chongli Yuan
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  4. Nurcin Celik
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Correspondence to Nurcin Celik .

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Editors and Affiliations

  1. Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA

    Erik Blasch

  2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  3. Information Directorate, Air Force Research Laboratory, Rome, NY, USA

    Alex Aved

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Damgacioglu, H., Celik, E., Yuan, C., Celik, N. (2018). Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-95504-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95503-2

  • Online ISBN: 978-3-319-95504-9

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