On the selection of appropriate distances for gene expression data clustering (2014)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1186/1471-2105-15-S2-S2
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: BioMed Central
- Publisher place: London
- Date published: 2014
- Source:
- Título: BMC Bioinformatics
- ISSN: 1471-2105
- Volume/Número/Paginação/Ano: v. 15, supl. 2, p. 1-17, 2014
- Conference titles: Asia Pacific Bioinformatics Conference - APBC
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto e COSTA, Ivan G. On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics. London: BioMed Central. Disponível em: https://doi.org/10.1186/1471-2105-15-S2-S2. Acesso em: 25 fev. 2026. , 2014 -
APA
Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2014). On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics. London: BioMed Central. doi:10.1186/1471-2105-15-S2-S2 -
NLM
Jaskowiak PA, Campello RJGB, Costa IG. On the selection of appropriate distances for gene expression data clustering [Internet]. BMC Bioinformatics. 2014 ; 15 1-17.[citado 2026 fev. 25 ] Available from: https://doi.org/10.1186/1471-2105-15-S2-S2 -
Vancouver
Jaskowiak PA, Campello RJGB, Costa IG. On the selection of appropriate distances for gene expression data clustering [Internet]. BMC Bioinformatics. 2014 ; 15 1-17.[citado 2026 fev. 25 ] Available from: https://doi.org/10.1186/1471-2105-15-S2-S2 - 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 o DOI: 10.1186/1471-2105-15-S2-S2 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
