Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models

A major challenge lies in discovering, emphasizing, and characterizing human gene-disease and gene-gene associations. The limitations of data on the role of human gene products in substance use disorder (SUD) makes it challenging to transition from genetic associations to actionable insights. The in...

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Vydáno v:PloS one Ročník 20; číslo 6; s. e0325201
Hlavní autoři: Castaneda, Everest U., Moore, Sharon, Bubier, Jason A., Grady, Stephen K., Langston, Michael A., Chesler, Elissa J., Baker, Erich J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 16.06.2025
Public Library of Science (PLoS)
Témata:
Alcohol use
Algorithms
Biology and Life Sciences
Computer and Information Sciences
Drug addiction
Drug use
Genes
Genetic aspects
Genetic Association Studies
Genetic Predisposition to Disease
Genomes
Genomics
Graphs
Humans
Influence
Knowledge representation
Mental disorders
Mental illness
Models, Genetic
Neurobiology
Ontology
Organisms
Physical Sciences
Proteins
Psychological aspects
Random walk
Research and Analysis Methods
Risk factors
Substance abuse
Substance use
Substance-Related Disorders - genetics
United States
ISSN:1932-6203, 1932-6203
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Shrnutí:A major challenge lies in discovering, emphasizing, and characterizing human gene-disease and gene-gene associations. The limitations of data on the role of human gene products in substance use disorder (SUD) makes it challenging to transition from genetic associations to actionable insights. The integration of data from multiple diverse sources, including information-dense studies in model organisms, has the potential to address this gap. We demonstrate a modified performance of the Random Walk with Restart algorithm when multi-species data is integrated in the heterogeneous network within the context of SUD. Additionally, our approach distinguishes among disparate pathways derived from the Kyoto Encyclopedia of Genes and Genomes. Thus, we conclude that direct incorporation of multi-species data to an aggregated heterogeneous knowledge graph can adjust RWR’s performance and enables users to discover new gene-disease and gene-gene associations.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0325201