Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models
Everest U. Castaneda, Sharon Moore, Jason A. Bubier, Stephen K. Grady, Michael A. Langston, Elissa J. Chesler, Erich J. Baker
TL;DR
This paper shows how combining data from multiple species can improve understanding of gene-disease links in substance use disorder using a network analysis method.
Contribution
The study introduces a modified Random Walk with Restart approach that integrates multi-species data for gene-disease association discovery in SUD.
Findings
Multi-species data integration improves the performance of the Random Walk with Restart algorithm in SUD contexts.
The approach helps distinguish between different biological pathways related to SUD.
Incorporating diverse data sources into a knowledge graph reveals new gene-disease and gene-gene associations.
Abstract
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…
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Taxonomy
TopicsBioinformatics and Genomic Networks · Genetic Associations and Epidemiology · Gene expression and cancer classification