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AGRA: analysis of gene ranking algorithms

Summary: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system tha...

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Detalles Bibliográficos
Autores principales: Kocbek, Simon, Sætre, Rune, Stiglic, Gregor, Kim, Jin-Dong, Pernek, Igor, Tsuruoka, Yoshimasa, Kokol, Peter, Ananiadou, Sophia, Tsujii, Jun'ichi
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072556/
https://www.ncbi.nlm.nih.gov/pubmed/21349873
http://dx.doi.org/10.1093/bioinformatics/btr097
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author Kocbek, Simon
Sætre, Rune
Stiglic, Gregor
Kim, Jin-Dong
Pernek, Igor
Tsuruoka, Yoshimasa
Kokol, Peter
Ananiadou, Sophia
Tsujii, Jun'ichi
author_facet Kocbek, Simon
Sætre, Rune
Stiglic, Gregor
Kim, Jin-Dong
Pernek, Igor
Tsuruoka, Yoshimasa
Kokol, Peter
Ananiadou, Sophia
Tsujii, Jun'ichi
author_sort Kocbek, Simon
collection PubMed
description Summary: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. Availability and Implementation: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. Contact: simon.kocbek@uni-mb.si
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spelling pubmed-30725562011-04-11 AGRA: analysis of gene ranking algorithms Kocbek, Simon Sætre, Rune Stiglic, Gregor Kim, Jin-Dong Pernek, Igor Tsuruoka, Yoshimasa Kokol, Peter Ananiadou, Sophia Tsujii, Jun'ichi Bioinformatics Applications Note Summary: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. Availability and Implementation: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. Contact: simon.kocbek@uni-mb.si Oxford University Press 2011-04-15 2011-02-23 /pmc/articles/PMC3072556/ /pubmed/21349873 http://dx.doi.org/10.1093/bioinformatics/btr097 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Kocbek, Simon
Sætre, Rune
Stiglic, Gregor
Kim, Jin-Dong
Pernek, Igor
Tsuruoka, Yoshimasa
Kokol, Peter
Ananiadou, Sophia
Tsujii, Jun'ichi
AGRA: analysis of gene ranking algorithms
title AGRA: analysis of gene ranking algorithms
title_full AGRA: analysis of gene ranking algorithms
title_fullStr AGRA: analysis of gene ranking algorithms
title_full_unstemmed AGRA: analysis of gene ranking algorithms
title_short AGRA: analysis of gene ranking algorithms
title_sort agra: analysis of gene ranking algorithms
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072556/
https://www.ncbi.nlm.nih.gov/pubmed/21349873
http://dx.doi.org/10.1093/bioinformatics/btr097
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