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Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks
Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245161/ https://www.ncbi.nlm.nih.gov/pubmed/22096235 http://dx.doi.org/10.1093/nar/gkr1050 |
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author | Haibe-Kains, Benjamin Olsen, Catharina Djebbari, Amira Bontempi, Gianluca Correll, Mick Bouton, Christopher Quackenbush, John |
author_facet | Haibe-Kains, Benjamin Olsen, Catharina Djebbari, Amira Bontempi, Gianluca Correll, Mick Bouton, Christopher Quackenbush, John |
author_sort | Haibe-Kains, Benjamin |
collection | PubMed |
description | Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ‘known’ interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/. |
format | Online Article Text |
id | pubmed-3245161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-32451612012-01-10 Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks Haibe-Kains, Benjamin Olsen, Catharina Djebbari, Amira Bontempi, Gianluca Correll, Mick Bouton, Christopher Quackenbush, John Nucleic Acids Res Articles Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ‘known’ interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/. Oxford University Press 2012-01 2011-11-16 /pmc/articles/PMC3245161/ /pubmed/22096235 http://dx.doi.org/10.1093/nar/gkr1050 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Haibe-Kains, Benjamin Olsen, Catharina Djebbari, Amira Bontempi, Gianluca Correll, Mick Bouton, Christopher Quackenbush, John Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title | Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title_full | Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title_fullStr | Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title_full_unstemmed | Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title_short | Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
title_sort | predictive networks: a flexible, open source, web application for integration and analysis of human gene networks |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245161/ https://www.ncbi.nlm.nih.gov/pubmed/22096235 http://dx.doi.org/10.1093/nar/gkr1050 |
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