Cargando…
iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease
Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify net...
Autores principales: | Duan, Ziheng, Dai, Yi, Hwang, Ahyeon, Lee, Cheyu, Xie, Kaichi, Xiao, Chutong, Xu, Min, Girgenti, Matthew J., Zhang, Jing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513318/ https://www.ncbi.nlm.nih.gov/pubmed/37695793 http://dx.doi.org/10.1371/journal.pcbi.1011444 |
Ejemplares similares
-
Hierarchical Graph Representation of Pharmacophore Models
por: Arthur, Garon, et al.
Publicado: (2020) -
Prioritizing tests of epistasis through hierarchical representation of genomic redundancies
por: Cowman, Tyler, et al.
Publicado: (2017) -
Mitigating Herding in Hierarchical Crowdsourcing Networks
por: Yu, Han, et al.
Publicado: (2016) -
FORGe: prioritizing variants for graph genomes
por: Pritt, Jacob, et al.
Publicado: (2018) -
Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks
por: Moutsinas, Giannis, et al.
Publicado: (2021)