Sequence motif identification and protein family classification using probabilistic trees (2005)
- Authors:
- USP affiliated authors: GALVES, JEFFERSON ANTONIO - IME ; LEONARDI, FLORENCIA GRACIELA - Interunidades em Bioinformática
- Unidades: IME; Interunidades em Bioinformática
- DOI: 10.1007/11532323_20
- Subjects: PROBABILIDADE; BIOINFORMÁTICA
- Keywords: Pfam Database; Suffix Tree; Acceptance Region; Family Classification; Protein Family Database
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Brazilian Symposium on Bioinformatics - BSB
- Este artigo NÃO possui versão em acesso aberto
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Status: Nenhuma versão em acesso aberto identificada -
ABNT
LEONARDI, Florencia Graciela e GALVES, Antonio. Sequence motif identification and protein family classification using probabilistic trees. 2005, Anais.. Berlin: Springer, 2005. Disponível em: https://doi.org/10.1007/11532323_20. Acesso em: 15 mar. 2026. -
APA
Leonardi, F. G., & Galves, A. (2005). Sequence motif identification and protein family classification using probabilistic trees. In Proceedings. Berlin: Springer. doi:10.1007/11532323_20 -
NLM
Leonardi FG, Galves A. Sequence motif identification and protein family classification using probabilistic trees [Internet]. Proceedings. 2005 ;[citado 2026 mar. 15 ] Available from: https://doi.org/10.1007/11532323_20 -
Vancouver
Leonardi FG, Galves A. Sequence motif identification and protein family classification using probabilistic trees [Internet]. Proceedings. 2005 ;[citado 2026 mar. 15 ] Available from: https://doi.org/10.1007/11532323_20 - Exponential inequalities for empirical unbounded context trees
- Random perturbations of stochastic processes with unbounded variable length memory
- Context tree selection and linguistic rhythm retrieval from written texts
- Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology
- Consistent model selection for the degree corrected stochastic blockmodel
- Change point detection for high-dimensional regression data with l1-regularization
- Context tree selection: a unifying view
- Some upper bounds for the rate of convergence of penalized likelihood context tree estimators
- Computationally efficient change point detection for high-dimensional regression
- Nonparametric statistical inference for the context tree of a stationary ergodic process
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