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MONET: a toolbox integrating top-performing methods for network modularization.
Tomasoni, Mattia; Gómez, Sergio; Crawford, Jake; Zhang, Weijia; Choobdar, Sarvenaz; Marbach, Daniel; Bergmann, Sven.
Afiliação
  • Tomasoni M; Department of Computational Biology, University of Lausanne.
  • Gómez S; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
  • Crawford J; Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain.
  • Zhang W; Department of Computer Science, Tufts University, MA.
  • Choobdar S; Graduate Group in Genomics and Computational Biology Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Marbach D; School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.
  • Bergmann S; Department of Computational Biology, University of Lausanne.
Bioinformatics ; 36(12): 3920-3921, 2020 06 01.
Article em En | MEDLINE | ID: mdl-32271874
SUMMARY: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the 'Disease Module Identification (DMI) DREAM Challenge', a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Bioinformatics Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Bioinformatics Ano de publicação: 2020 Tipo de documento: Article