Autor segons l'article: Tomasoni, Mattia; Gomez, Sergio; Crawford, Jake; Zhang, Weijia; Choobdar, Sarvenaz; Marbach, Daniel; Bergmann, Sven
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Gómez Jiménez, Sergio
Paraules clau: Software Community structure Algorithms
Resum: © 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 1367-4803
Adreça de correu electrònic de l'autor: sergio.gomez@urv.cat
Identificador de l'autor: 0000-0003-1820-0062
Pàgina final: 3921
Data d'alta del registre: 2024-10-26
Volum de revista: 36
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://academic.oup.com/bioinformatics/article/36/12/3920/5818484
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Bioinformatics. 36 (12): 3920-3921
Referència 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 de l'article: 10.1093/bioinformatics/btaa236
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Pàgina inicial: 3920
Tipus de publicació: Journal Publications
Repositori URV