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Resource:DONE: Detection of Outlier NEurons

Name: Resource:DONE: Detection of Outlier NEurons
Description: Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011).

This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.

References:

  • L. d. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser. Beyond the average: Detecting global singular nodes from local features in complex networks, Europhysics Letters, 87, 1 (2009)
  • C. Echtermeyer, L. d. Fontoura Costa, F. A. Rodrigues, M. Kaiser. Automatic Network Fingerprinting Through Single-Node Motifs, PLoS ONE 6, 9 (2011)
  • K. d. Zawadzki, C. Feenders, M. P. Viana, M. Kaiser, and L. d. Fontoura Costa. Morphological Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells, Neuroinformatics (2012)[1]
Other Name(s): Detection of Outlier NEurons
Parent Organization: Newcastle University; Newcastle upon Tyne; United Kingdom
Supporting Agency: FAPESP, CNPq, EPSRC, Resource:Code Analysis Repository and Modelling for e-Neuroscience, Korean Ministry of Education Science and Technology
Related to: Resource:NeuroMorpho.Org, Resource:NITRC
Resource Type(s): Software application
Keywords: neuron, feature-space, archetype, outlier, MATLAB, neuromorphometry, Computational neuroscience
Grant: 05/00587-5, 301303/06-1, 2010/01994-1, 2010/16310-0, 573583/2008-0, EP/G03950X/1, R32-10142
Abbreviation: DONE
Resource: Resource
URL: http://www.biological-networks.org/p/outliers/
PMID: PMID 22615032
Publication link: http://www.biological-networks.org/pubs/Zawadzki2012.pdf
Availability: GNU General Public License
Id: nlx_144348
Link to OWL / RDF: Download this content as OWL/RDF

Curation status: Curated

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References

  1. Zawadzki K et al. (2012) Morphological homogeneity of neurons: searching for outlier neuronal cells. Neuroinformatics 10: 379-89 PubMed

Notes

This page uses this default form:Resource

Alt. NITRC URL: http://www.nitrc.org/projects/done/

Contributors

Aarnaud, Cfeenders, Memartone



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Facts about Resource:DONE: Detection of Outlier NEuronsRDF feed
AbbrevDONE  +
AvailabilityGNU General Public License  +
CurationStatuscurated  +
DefiningCitationhttp://www.biological-networks.org/p/outliers/  +
DefinitionSoftware that performs a morphology-based Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011).

This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.

References:

  • L. d. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser. Beyond the average: Detecting global singular nodes from local features in complex networks, Europhysics Letters, 87, 1 (2009)
  • C. Echtermeyer, L. d. Fontoura Costa, F. A. Rodrigues, M. Kaiser. Automatic Network Fingerprinting Through Single-Node Motifs, PLoS ONE 6, 9 (2011)
  • K. d. Zawadzki, C. Feenders, M. P. Viana, M. Kaiser, and L. d. Fontoura Costa. Morphological Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells, Neuroinformatics (2012) er Neuronal Cells, Neuroinformatics (2012)
GrantCategory:05/00587-5   +, Category:301303/06-1   +, Category:2010/01994-1   +, Category:2010/16310-0   +, Category:573583/2008-0   +, Category:EP/G03950X/1   +, and Category:R32-10142   +
Has default formThis property is a special property in this wiki.Resource  +
Has roleSoftware application  +
Idnlx_144348  +
Is part ofNewcastle University; Newcastle upon Tyne; United Kingdom  +
KeywordsNeuron  +, Feature-space  +, Archetype  +, Outlier  +, MATLAB  +, Neuromorphometry  +, and Computational neuroscience  +
LabelResource:DONE: Detection of Outlier NEurons  +
ModifiedDate2 November 2013  +
PMID22615032  +
Page has default formThis property is a special property in this wiki.Resource  +
PublicationLinkhttp://www.biological-networks.org/pubs/Zawadzki2012.pdf  +
RelatedToResource:NeuroMorpho.Org  +, and Resource:NITRC  +
SuperCategoryResource  +
Supporting AgencyFAPESP  +, CNPq  +, EPSRC  +, Resource:Code Analysis Repository and Modelling for e-Neuroscience  +, and Korean Ministry of Education Science and Technology  +
SynonymDetection of Outlier NEurons  +