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AMIC@: All MIcroarray Clusterings @ once
The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algor...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447730/ https://www.ncbi.nlm.nih.gov/pubmed/18477631 http://dx.doi.org/10.1093/nar/gkn265 |
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author | Geraci, Filippo Pellegrini, Marco Renda, M. Elena |
author_facet | Geraci, Filippo Pellegrini, Marco Renda, M. Elena |
author_sort | Geraci, Filippo |
collection | PubMed |
description | The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algorithms and inspection of results. Among the salient features AMIC@ offers, there are: (i) automatic file format detection, (ii) suggestions on the number of clusters using a variant of the stability-based method of Tibshirani et al. (iii) intuitive visual inspection of the data via heatmaps and (iv) measurements of the clustering quality using cluster homogeneity. Large data sets can be processed efficiently by selecting algorithms (such as FPF-SB and k-Boost), specifically designed for this purpose. In case of very large data sets, the user can opt for a batch-mode use of the system by means of the Clustering wizard that runs all algorithms at once and delivers the results via email. AMIC@ is freely available and open to all users with no login requirement at the following URL http://bioalgo.iit.cnr.it/amica. |
format | Text |
id | pubmed-2447730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-24477302008-07-09 AMIC@: All MIcroarray Clusterings @ once Geraci, Filippo Pellegrini, Marco Renda, M. Elena Nucleic Acids Res Articles The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algorithms and inspection of results. Among the salient features AMIC@ offers, there are: (i) automatic file format detection, (ii) suggestions on the number of clusters using a variant of the stability-based method of Tibshirani et al. (iii) intuitive visual inspection of the data via heatmaps and (iv) measurements of the clustering quality using cluster homogeneity. Large data sets can be processed efficiently by selecting algorithms (such as FPF-SB and k-Boost), specifically designed for this purpose. In case of very large data sets, the user can opt for a batch-mode use of the system by means of the Clustering wizard that runs all algorithms at once and delivers the results via email. AMIC@ is freely available and open to all users with no login requirement at the following URL http://bioalgo.iit.cnr.it/amica. Oxford University Press 2008-07-01 2008-05-13 /pmc/articles/PMC2447730/ /pubmed/18477631 http://dx.doi.org/10.1093/nar/gkn265 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Geraci, Filippo Pellegrini, Marco Renda, M. Elena AMIC@: All MIcroarray Clusterings @ once |
title | AMIC@: All MIcroarray Clusterings @ once |
title_full | AMIC@: All MIcroarray Clusterings @ once |
title_fullStr | AMIC@: All MIcroarray Clusterings @ once |
title_full_unstemmed | AMIC@: All MIcroarray Clusterings @ once |
title_short | AMIC@: All MIcroarray Clusterings @ once |
title_sort | amic@: all microarray clusterings @ once |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447730/ https://www.ncbi.nlm.nih.gov/pubmed/18477631 http://dx.doi.org/10.1093/nar/gkn265 |
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