Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis (2013)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1109/TCBB.2013.9
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher place: Los Alamitos
- Date published: 2013
- Source:
- Título: IEEE/ACM Transactions on Computational Biology and Bioinformatics
- ISSN: 1545-5963
- Volume/Número/Paginação/Ano: v. 10, n. 4, p. 845-857, jul./ago. 2013
- Este artigo possui versão em acesso aberto
- URL de acesso aberto
- Versão do Documento: Versão submetida (Pré-print)
-
Status: Artigo possui versão em acesso aberto em repositório (Green Open Access) -
ABNT
JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto e COSTA, Ivan G. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 10, n. 4, p. 845-857, 2013Tradução . . Disponível em: https://doi.org/10.1109/TCBB.2013.9. Acesso em: 13 mar. 2026. -
APA
Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2013). Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10( 4), 845-857. doi:10.1109/TCBB.2013.9 -
NLM
Jaskowiak PA, Campello RJGB, Costa IG. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2013 ; 10( 4): 845-857.[citado 2026 mar. 13 ] Available from: https://doi.org/10.1109/TCBB.2013.9 -
Vancouver
Jaskowiak PA, Campello RJGB, Costa IG. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2013 ; 10( 4): 845-857.[citado 2026 mar. 13 ] Available from: https://doi.org/10.1109/TCBB.2013.9 - Similarity measures for comparing biclusterings
- Density-based clustering validation
- Relative validity criteria for community mining algorithms
- Active learning strategies for semi-supervised DBSCAN
- On the evaluation of outlier detection and one-class classification methods
- An introduction to models based on Laguerre, Kautz and other related orthonormal functions - part II: non-linear models
- Evaluating correlation coefficients for clustering gene expression profiles of cancer
- A simpler and more accurate AUTO-HDS framework for clustering and visualization of biological data
- Exact search directions for optimization of linear and nonlinear models based on generalized orthonormal functions
- A cluster based hybrid feature selection approach
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas