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MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules.
Ahmed, Rafsan; Baali, Ilyes; Erten, Cesim; Hoxha, Evis; Kazan, Hilal.
Afiliação
  • Ahmed R; Electrical and Computer Engineering Graduate Program, Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey.
  • Baali I; Electrical and Computer Engineering Graduate Program, Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey.
  • Erten C; Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey.
  • Hoxha E; Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey.
  • Kazan H; Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey.
Bioinformatics ; 36(3): 872-879, 2020 02 01.
Article em En | MEDLINE | ID: mdl-31432076
ABSTRACT
MOTIVATION Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.

RESULTS:

We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at https//github.com/abu-compbio/MEXCOwalk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / Neoplasias Tipo de estudo: Clinical_trials / Prognostic_studies Limite: Humans Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Turquia

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / Neoplasias Tipo de estudo: Clinical_trials / Prognostic_studies Limite: Humans Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Turquia