An integrative and systems biology approach to interpreting, prioritizing, and analyzing the genetics of complex disorders.

Substance use disorders (SUD) are a significant societal burden. Even with modern high-throughput technological advances, the underlying genetic architecture of SUDs is not fully comprehended, which is due to the substantial number of genes implicated, underpowered studies, and sparse association data. Hence, highly scalable graph algorithms, which combine features among all aspects of omics data, are leveraged to investigate the genomic underpinnings of this complex and multifactorial disorder. Described in these studies is an investigation of classes of SUDs, namely alcohol, cocaine, nicotine, and opium, through exploiting network representation learning. A method for generating high-fidelity functional networks is outlined, and further, it is used for the quantification of genetic distances between classes of SUDs and cardiovascular disease. Multi-omics and multi-species graphs are integrated to prioritize SUD-associated genes. Combined, these approaches yield techniques in which to compare, comprehend, and confirm genes involved in the complex genetic structure of SUDs.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2025

Author dc:creator

• Castaneda, Everest U. (Everest Uriel), 1990-

Advisor dc:contributor.advisor

• Kearney, Christopher Michel, 1958-

• Model organism supplementation.
• Homological graph.
• Heterogeneous graph.
• Disease-associated prioritization.
• Substance use disorder.
• Functional fingerprint.
• Kyoto encyclopedia of genes and genomes (KEGG) markup language (KGML) parser.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/13786

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/13786