Robust disease module mining via enumeration of diverse prize-collecting Steiner trees.
Bioinformatics
; 38(6): 1600-1606, 2022 03 04.
Article
em En
| MEDLINE
| ID: mdl-34984440
ABSTRACT
MOTIVATION Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i.e. the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the widespread adoption of DMMMs in the biomedical sciences. RESULTS:
To overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG (Kyoto Encyclopedia of Genes and Genomes) gene set enrichment scores and overlap with DisGeNET disease genes. AVAILABILITY AND IMPLEMENTATION A Python 3 implementation and scripts to reproduce the results reported in this article are available on GitHub https//github.com/bionetslab/robust, https//github.com/bionetslab/robust-eval. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Base de dados:
MEDLINE
Assunto principal:
Árvores
/
Algoritmos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article