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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Main Authors:	Kocbek, Simon, Sætre, Rune, Stiglic, Gregor, Kim, Jin-Dong, Pernek, Igor, Tsuruoka, Yoshimasa, Kokol, Peter, Ananiadou, Sophia, Tsujii, Jun'ichi
Format:	Online
Language:	English
Published:	Oxford University Press 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072556/

id	pubmed-3072556
recordtype	oai_dc
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 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. 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kocbek, Simon Sætre, Rune Stiglic, Gregor Kim, Jin-Dong Pernek, Igor Tsuruoka, Yoshimasa Kokol, Peter Ananiadou, Sophia Tsujii, Jun'ichi
spellingShingle	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
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
title	AGRA: analysis of gene ranking algorithms
title_short	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_sort	agra: analysis of gene ranking algorithms
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.
publisher	Oxford University Press
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072556/
_version_	1611448720035938304

AGRA: analysis of gene ranking algorithms

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