DOI
https://doi.org/10.25772/M80W-G210
Defense Date
2017
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Computer Science
First Advisor
Preetam Ghosh, PhD
Second Advisor
Milos Manic, PhD
Third Advisor
Devanand Sarkar, MBBS, PhD
Fourth Advisor
Vladimir Vladimirov, MD, PhD
Fifth Advisor
Thang Dinh, PhD
Abstract
miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-9-2017
Included in
Computational Biology Commons, Discrete Mathematics and Combinatorics Commons, Numerical Analysis and Scientific Computing Commons, Other Applied Mathematics Commons, Other Computer Sciences Commons, Systems Biology Commons, Theory and Algorithms Commons