{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/13786"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/13786","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"An integrative and systems biology approach to interpreting, prioritizing, and analyzing the genetics of complex disorders.","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Castaneda, Everest U. (Everest Uriel), 1990-"],"institution":"Baylor University.","degree_name":"Ph.D.","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Kearney, Christopher Michel, 1958-"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-24T01:07:52Z","subjects":["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."],"languages":["en"],"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. 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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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An integrative and systems biology approach to interpreting, prioritizing, and analyzing the genetics of complex disorders."]}]}],"canonical_facts":{"dc:contributor.advisor":["Kearney, Christopher Michel, 1958-"],"dc:creator":["Castaneda, Everest U. (Everest Uriel), 1990-"],"dc:date.accessioned":["2025-09-05T17:23:08Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["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. 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