Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Final.pdf (8.71 MB)

ETD Abstract Container

Abstract Header

Computational Methods to Characterize the Etiology of Complex Diseases at Multiple Levels

Elmansy, Dalia F.
http://rave.ohiolink.edu/etdc/view?acc_num=case1583416431321447

Abstract Details

2020, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
Complex diseases, like cancer or Type II Diabetes, result from the interplay between multiple genetic factors at different cellular levels as well as environmental factors. Deciphering the etiology of complex diseases mandates characterizing the function of many underlying factors and the relationships between these factors. The availability of a plethora of omic data at a genomic scale and the availability of disease-associated data from a broad range of populations present a valuable resource. When such wealth of data is utilized by integrative and efficient computational methods and robust statistical frameworks, it could help in elucidating the etiology of complex diseases and the realization of precision medicine. Due to the complexity of biological systems; the intricacy of inter-genomic interactions, the obscuring effect of many confounding factors, the high dimensionality and the high degree of noise in the data, effective use of omic data for accurate disease risk prediction faces important challenges. This problem is especially clear when dealing with complex diseases like cancer. In this thesis, we utilize multiple types of omic data as well as population-specific data and develop integrative computational methods to characterize the interplay between various factors that underlie complex diseases. We perform computational analyses at multiple levels, capture functional commonalities of disease-associated variants across different populations and model the interplay between disease-associated genes at the cellular level. We model and spot distortion in omic data by discovering new features that mitigate its negative impact on the predictive ability of biomarkers, hence improving the accuracy of disease risk prediction.
Mehmet Koyuturk (Committee Chair)
Vinay Varadan (Committee Member)
Erman Ayday (Committee Member)
Ming-Chun Huang (Committee Member)
An Wang (Committee Member)
130 p.
Bioinformatics; Computer Science
Complex Diseases; Type II Diabetes variants; Overlap Analysis; SNPs; population specific functional annotation of genes; ethnicity and T2D; network analysis; Machine learning; Distortion in omics data; cancer risk prediction; assessment; cancer purity estimation

Recommended Citations

Citations

  • Elmansy, D. F. (2020). Computational Methods to Characterize the Etiology of Complex Diseases at Multiple Levels [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1583416431321447

    APA Style (7th edition)

  • Elmansy, Dalia. Computational Methods to Characterize the Etiology of Complex Diseases at Multiple Levels. 2020. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1583416431321447.

    MLA Style (8th edition)

  • Elmansy, Dalia. "Computational Methods to Characterize the Etiology of Complex Diseases at Multiple Levels." Doctoral dissertation, Case Western Reserve University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1583416431321447

    Chicago Manual of Style (17th edition)

case1583416431321447
211
© 2020, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.