standards > model/format > DOI:10.25504/FAIRsharing.h6c48k


ready KEGG Mark-up Language

Abbreviation: KGML


General Information
The KEGG Markup Language (KGML) is an exchange format of the KEGG pathway maps, which is converted from internally used KGML+ (KGML+SVG) format. KGML enables automatic drawing of KEGG pathways and provides facilities for computational analysis and modeling of gene/protein networks and chemical networks.



How to cite this record FAIRsharing.org: KGML; KEGG Mark-up Language; DOI: https://doi.org/10.25504/FAIRsharing.h6c48k; Last edited: Jan. 8, 2019, 1:37 p.m.; Last accessed: Aug 20 2019 12:06 a.m.


Record updated: March 15, 2018, 10:13 a.m. by The FAIRsharing Team.

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General

Additional Information


Tools

    No tools defined


Schemas

No XSD schemas defined


Access / Retrieve Data

Conditions of Use



REST Web Services



Publications

KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats.

Wrzodek C,Drager A,Zell A
Bioinformatics 2011

View Paper (PubMed) View Publication

Related Standards

Reporting Guidelines

No guidelines defined

Terminology Artifacts

No semantic standards defined

Models and Formats

Identifier Schemas

No identifier schema standards defined

Metrics

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Implementing Databases (2)
Integrated Pathway Analysis and Visualization System
iPAVS provides a collection of highly-structured manually curated human pathway data, it also integrates biological pathway information from several public databases and provides several tools to manipulate, filter, browse, search, analyze, visualize and compare the integrated pathway resources.

Genome-wide Integrated Analysis of gene Networks in Tissues 2.0
GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses.

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