Autor según el artículo: Tomasoni, Mattia; Gomez, Sergio; Crawford, Jake; Zhang, Weijia; Choobdar, Sarvenaz; Marbach, Daniel; Bergmann, Sven
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Gómez Jiménez, Sergio
Palabras clave: Software Community structure Algorithms
Resumen: © The Author(s) 2020. Published by Oxford University Press. 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.
Áreas temáticas: Statistics and probability Statistics & probability Odontología Nutrição Molecular biology Medicina veterinaria Medicina ii Medicina i Mathematics, interdisciplinary applications Mathematical & computational biology Matemática / probabilidade e estatística Interdisciplinar General medicine Engenharias iv Economia Computer science, interdisciplinary applications Computer science applications Computational theory and mathematics Computational mathematics Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência da computação Biotecnología Biotechnology & applied microbiology Biology, miscellaneous Biodiversidade Biochemistry Biochemical research methods Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 1367-4803
Direcció de correo del autor: sergio.gomez@urv.cat
Identificador del autor: 0000-0003-1820-0062
Página final: 3921
Fecha de alta del registro: 2024-10-26
Volumen de revista: 36
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://academic.oup.com/bioinformatics/article/36/12/3920/5818484
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Bioinformatics. 36 (12): 3920-3921
Referencia de l'ítem segons les normes APA: Tomasoni, Mattia; Gomez, Sergio; Crawford, Jake; Zhang, Weijia; Choobdar, Sarvenaz; Marbach, Daniel; Bergmann, Sven (2020). MONET: a toolbox integrating top-performing methods for network modularization. Bioinformatics, 36(12), 3920-3921. DOI: 10.1093/bioinformatics/btaa236
DOI del artículo: 10.1093/bioinformatics/btaa236
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2020
Página inicial: 3920
Tipo de publicación: Journal Publications
Repositori URV