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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...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2008
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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. |
format | Text |
id | pubmed-3041521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
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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