NIFTI: An evolutionary approach for finding number of clusters in microarray data
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
NIFTI: An evolutionary approach for finding number of clusters in microarray data ; volume:10 ; number:1 ; day:30 ; month:1 ; year:2009 ; pages:1-13 ; date:12.2009
BMC bioinformatics ; 10, Heft 1 (30.1.2009), 1-13, 12.2009
- Creator
-
Jonnalagadda, Sudhakar
- Contributor
-
Srinivasan, Rajagopalan
SpringerLink (Online service)
- DOI
-
10.1186/1471-2105-10-40
- URN
-
urn:nbn:de:1111-2016021710663
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Jonnalagadda, Sudhakar
- Srinivasan, Rajagopalan
- SpringerLink (Online service)
Other Objects (12)
Finding clusters in parallel universes
Reproducible Clusters from Microarray Research: Whither?
Finding consistent disease subnetworks across microarray datasets
Visualization methods for statistical analysis of microarray clusters
A unified framework for finding differentially expressed genes from microarray experiments
Evolutionary impact of copy number variation rates
A tool for finding inclusion clusters in steel SEM specimens
CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data
Frustration-induced magic number clusters of colloidal magnetic particles
Thresher: determining the number of clusters while removing outliers
Magic number colloidal clusters as minimum free energy structures
Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray
Finding clusters in parallel universes
Reproducible Clusters from Microarray Research: Whither?
Finding consistent disease subnetworks across microarray datasets
Visualization methods for statistical analysis of microarray clusters
A unified framework for finding differentially expressed genes from microarray experiments
Evolutionary impact of copy number variation rates
A tool for finding inclusion clusters in steel SEM specimens
CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data
Frustration-induced magic number clusters of colloidal magnetic particles
Thresher: determining the number of clusters while removing outliers
Magic number colloidal clusters as minimum free energy structures
Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray
Finding clusters in parallel universes
Reproducible Clusters from Microarray Research: Whither?
Finding consistent disease subnetworks across microarray datasets
Visualization methods for statistical analysis of microarray clusters
A unified framework for finding differentially expressed genes from microarray experiments
Evolutionary impact of copy number variation rates
A tool for finding inclusion clusters in steel SEM specimens
CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data
Frustration-induced magic number clusters of colloidal magnetic particles
Thresher: determining the number of clusters while removing outliers
Magic number colloidal clusters as minimum free energy structures