Abstract
Gene regulatory networks are at the core of many biological processes, dictating how a wide range of genes, and their products, interact with one another. Gaining further understanding on how these networks are structured and how they function within the body can aid in deriving meaningful insights on the body’s inner-workings when exposed to any number of conditions. Unfortunately, the information gathered within some studies may be lacking in, for example, sample size or measurements taken at multiple time-points. This work addresses these issues and outlines a pipeline which navigates these smaller collections of data in order to still extract meaningful insights – particularly on gene regulatory networks for a dataset involving various exercise interventions. The work described below demonstrates how a number of statistical techniques, tools, and literature review can yield numerous findings to include: genes relevant to exercise, the relationships between those genes and how they vary across the exercise interventions, and the longevity of these gene regulatory networks.
Library of Congress Subject Headings
Gene regulatory networks--Research; Constraints (Artificial intelligence); Exercise--Physiological aspects
Publication Date
3-26-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Bioinformatics (MS)
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences
College
College of Science
Advisor
Gary Skuse
Advisor/Committee Member
Gordon Broderick
Advisor/Committee Member
Gregory Babbitt
Recommended Citation
Moore, Nadia, "Bioinformatics Pipeline for Gene Regulatory Network Discovery: A Case Study with Exercise Response" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12049
Campus
RIT – Main Campus
Plan Codes
BIOINFO-MS
