An efficient and effective method to identify significantly perturbed subnetworks in cancer.
Journal
Nature computational science
ISSN: 2662-8457
Titre abrégé: Nat Comput Sci
Pays: United States
ID NLM: 101775476
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
medline:
1
1
2021
pubmed:
1
1
2021
entrez:
22
6
2023
Statut:
ppublish
Résumé
The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.
Identifiants
pubmed: 37346964
doi: 10.1038/s43588-020-00009-4
pmc: PMC10284573
mid: NIHMS1857839
doi:
Types de publication
Journal Article
Langues
eng
Pagination
79-88Subventions
Organisme : NIAID NIH HHS
ID : R01 AI125982
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA241123
Pays : United States
Déclaration de conflit d'intérêts
Competing interests The authors declare no competing interests.
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