Theses and Dissertations

Advisor

Popescu, George V.

Committee Member

Hoffmann, Federico G.

Committee Member

Peterson, Daniel G.

Date of Degree

8-9-2022

Document Type

Graduate Thesis - Open Access

Major

Computational Biology

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics data can be standardized between contact points in various software applications. Further, network inference or network comparison algorithmic designs may involve interchangeable operations given the structure of their implementations. Network comparison and inference methods can also guide transfer-of-knowledge between model organisms and those with less knowledge base.

Share

COinS