G2D statistics

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Citations per year

Number of citations per year for the bioinformatics software tool G2D
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Tool usage distribution map

This map represents all the scientific publications referring to G2D per scientific context
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Associated diseases

This word cloud represents G2D usage per disease context
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Popular tool citations

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Protocols

G2D specifications

Information


Unique identifier OMICS_22630
Name G2D
Alternative name Genes to Diseases
Interface Web user interface
Restrictions to use None
Computer skills Basic
Version 5.0
Stability Stable
Maintained No

Maintainers


This tool is not available anymore.

Additional information


http://g2d2.ogic.ca/info/history.html#v5.0

Publication for Genes to Diseases

G2D citations

 (6)
library_books

Bioinformatics challenges for personalized medicine

2011
Bioinformatics
PMCID: 3117361
PMID: 21596790
DOI: 10.1093/bioinformatics/btr295

[…] s or proteins and their orthologs in mouse models (). ENDEAVOUR is trained on genes involved in a known biological process and ranks candidate genes after considering several genomic data sources (). G2D prioritization strategy is based on a combination of data mining on biomedical databases and sequence features (). PolySearch analyzes biomedical databases to build relationships between diseases, […]

library_books

Gene prioritization in Type 2 Diabetes using domain interactions and network analysis

2010
BMC Genomics
PMCID: 2824729
PMID: 20122255
DOI: 10.1186/1471-2164-11-84

[…] he chromosomal region and evaluates their possible relation to the disease. The default E-value (<10e-10) was used here for extracting the putative disease genes based on the "known gene" protocol of G2D. The "known gene" takes already well-known disease genes to be associated with disease of interest for prediction. Thus, here we have considered the same set of "known gene" in T2D used by the Gen […]

library_books

Gene–disease relationship discovery based on model driven data integration and database view definition

2008
Bioinformatics
PMCID: 2639000
PMID: 19042916
DOI: 10.1093/bioinformatics/btn612

[…] nstance, the GeneSeeker system (van Driel et al., ) and the GeneSorter functionality proposed by UCSC Genome Browser (Kent et al., ) allow experts to test various hypotheses on criteria that can link genes to diseases. An example is found in Tiffin et al. (), who developed a strategy to identify genes expressed in tissue affected by a disease. Hence, candidate genes are selected if their correspon […]

library_books

Genes to Diseases (G2D) Computational Method to Identify Asthma Candidate Genes

2008
PLoS One
PMCID: 2488373
PMID: 18682798
DOI: 10.1371/journal.pone.0002907

[…] This study proposes a novel approach for the selection of candidate genes for asthma association studies using the computational G2D tool to find genetic determinants for this disease. Based on a genome-wide scan performed in an asthmatic familial sample from the SLSJ founder population, we selected the two best-linked regions […]

library_books

Update of the G2D tool for prioritization of gene candidates to inherited diseases

2007
Nucleic Acids Res
PMCID: 1933178
PMID: 17478516
DOI: 10.1093/nar/gkm223

[…] The G2D web server allows users to prioritize candidate genes for practically any disease phenotype in OMIM through three different methods. The results of benchmarking the methods on 227 diseases that ha […]

call_split

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

2006
Nucleic Acids Res
PMCID: 1475747
PMID: 16757574
DOI: 10.1093/nar/gkl381
call_split See protocol

[…] ked list from SUSPECTS, which took both the region of interest and the relevant training set as input in each case. The target gene was in the top 5% of the ranked list 87 times out of 156 (56%) (,). G2D () (,): This system scores all terms in GO according to their relevance to each disease starting from MEDLINE queries featuring the name of the disease. This is done by relating symptoms to GO ter […]


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G2D institution(s)
Ontario Genomics Innovation Centre, Ottawa Health Research Institute, Ottawa, ON, Canada; Institut für Epidemiologie, GSF Forschungszentrum für Umwelt undGesundheit Ingolstadter, Neuherberg/Munich, Germany; European Molecular Biology Laboratory, Heidelberg, Germany; Department of Bioinformatics, Max Delbrück Center for Molecular Medicine, Berlin, Germany

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