ClassyFire statistics

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Associated diseases

Associated diseases

ClassyFire specifications

Information


Unique identifier OMICS_24908
Name ClassyFire
Interface Web user interface
Restrictions to use None
Input data SMILES,SDF,InChI,IUPAC name,FASTA
Output data Standard chemical classification data, a list of chemical substituents, and a secondary attribute called the “Molecular Framework” (for many compounds).
Output format HTML+(JSON,SDF,CSV)
Computer skills Basic
Version 1.0
Stability Stable
Maintained Yes

Additional information


http://classyfire.wishartlab.com/access

Publication for ClassyFire

ClassyFire in publications

 (3)
PMCID: 5761900
PMID: 29320554
DOI: 10.1371/journal.pone.0191006

[…] disease information in which the protein targets may be involved. drugbank database [] was also mined to find-out the known target proteins., an automated and rapid chemical classification method “classyfire” [] was used to assign a chemical class to the phytochemicals. the query is mapped to the various classes based on its features that are calculated using superstructure-search operations […]

PMCID: 5462667
DOI: 10.1186/s13321-017-0225-z

[…] molecules into categories []. an example of this type of publications is work by bobach et al. describing a rule-based definition of chemical classes to classify compounds into classes [] or the classyfire software [] developed in the wishart’s group allowing chemists to perform large-scale automated chemical classification based on a structure-based chemical taxonomy consisting of over 4800 […]

PMCID: 4655007
PMID: 26617479
DOI: 10.1007/s11306-015-0894-4

[…] individual classifiers, which can be extremely computationally demanding especially if these classifiers involve complex models such as nonlinear svms. to address this issue, we have developed the classyfire r package for the implementation of ensemble svm training with bootstrapping and rigorous performance evaluation via a handful of high-level functions. the key to this package is a novel […]


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ClassyFire institution(s)
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada; Jobber – Field Service Software, Edmonton, AB, Canada; Department of Computing Science, University of Alberta, Edmonton, AB, Canada; National Research Council, National Institute for Nanotechnology (NINT), Edmonton, AB, Canada; Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Civic Campus, Ottawa, ON, Canada; European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Department of Bioengineering, University of California, La Jolla, San Diego, CA, USA; Department of Health and Human Services, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; Department of Computing Science, Alberta Innovates Centre for Machine Learning (AICML), University of Alberta, Edmonton, AB, Canada; The Metabolomics Innovation Center, University of Alberta, Edmonton, AB, Canada
ClassyFire funding source(s)
Supported by Genome Canada, Genome Alberta, The Canadian Institutes of Health Research, Alberta Innovates, The National Research Council and The National Institute of Nanotechnology.

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