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GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research

With the overwhelming volume of genomic and molecular information available on many databases nowadays, researchers need from bioinformaticians more than encouragement to refine their searches. We present here GeneRanker, an online system that allows researchers to obtain a ranked list of genes pote...

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Detalles Bibliográficos
Autores principales: Gonzalez, Graciela, Uribe, Juan C., Armstrong, Brock, McDonough, Wendy, Berens, Michael E.
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041521/
https://www.ncbi.nlm.nih.gov/pubmed/21347122
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author Gonzalez, Graciela
Uribe, Juan C.
Armstrong, Brock
McDonough, Wendy
Berens, Michael E.
author_facet Gonzalez, Graciela
Uribe, Juan C.
Armstrong, Brock
McDonough, Wendy
Berens, Michael E.
author_sort Gonzalez, Graciela
collection PubMed
description With the overwhelming volume of genomic and molecular information available on many databases nowadays, researchers need from bioinformaticians more than encouragement to refine their searches. We present here GeneRanker, an online system that allows researchers to obtain a ranked list of genes potentially related to a specific disease or biological process by combining gene-disease (or genebiological process) associations with protein-protein interactions extracted from the literature, using computational analysis of the protein network topology to more accurately rank the predicted associations. GeneRanker was evaluated in the context of brain cancer research, and is freely available online at http://www.generanker.org.
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spelling pubmed-30415212011-02-23 GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research Gonzalez, Graciela Uribe, Juan C. Armstrong, Brock McDonough, Wendy Berens, Michael E. Summit on Translat Bioinforma Articles With the overwhelming volume of genomic and molecular information available on many databases nowadays, researchers need from bioinformaticians more than encouragement to refine their searches. We present here GeneRanker, an online system that allows researchers to obtain a ranked list of genes potentially related to a specific disease or biological process by combining gene-disease (or genebiological process) associations with protein-protein interactions extracted from the literature, using computational analysis of the protein network topology to more accurately rank the predicted associations. GeneRanker was evaluated in the context of brain cancer research, and is freely available online at http://www.generanker.org. American Medical Informatics Association 2008-03-01 /pmc/articles/PMC3041521/ /pubmed/21347122 Text en ©2008 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Gonzalez, Graciela
Uribe, Juan C.
Armstrong, Brock
McDonough, Wendy
Berens, Michael E.
GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title_full GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title_fullStr GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title_full_unstemmed GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title_short GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research
title_sort generanker: an online system for predicting gene-disease associations for translational research
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041521/
https://www.ncbi.nlm.nih.gov/pubmed/21347122
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