A tutorial to identify nonlinear associations in gene expression time series data (2014)
- Authors:
- Autor USP: FUJITA, ANDRÉ - IME
- Unidade: IME
- DOI: 10.1007/978-1-4939-0805-9_8
- Subjects: BIOINFORMÁTICA; ANÁLISE DE SÉRIES TEMPORAIS; ANÁLISE MULTIVARIADA
- Keywords: regulatory network; systems biology; Granger causality; vector autoregressive model; nonlinear vector autoregressive model
- Language: Inglês
- Imprenta:
- Publisher: Humana Press
- Publisher place: New York
- Date published: 2014
- Source:
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
FUJITA, André e MIYANO, Satoru. A tutorial to identify nonlinear associations in gene expression time series data. Transcription factor regulatory networks: methods and protocols. Tradução . New York: Humana Press, 2014. . Disponível em: https://doi.org/10.1007/978-1-4939-0805-9_8. Acesso em: 02 nov. 2024. -
APA
Fujita, A., & Miyano, S. (2014). A tutorial to identify nonlinear associations in gene expression time series data. In Transcription factor regulatory networks: methods and protocols. New York: Humana Press. doi:10.1007/978-1-4939-0805-9_8 -
NLM
Fujita A, Miyano S. A tutorial to identify nonlinear associations in gene expression time series data [Internet]. In: Transcription factor regulatory networks: methods and protocols. New York: Humana Press; 2014. [citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/978-1-4939-0805-9_8 -
Vancouver
Fujita A, Miyano S. A tutorial to identify nonlinear associations in gene expression time series data [Internet]. In: Transcription factor regulatory networks: methods and protocols. New York: Humana Press; 2014. [citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/978-1-4939-0805-9_8 - Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias
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Informações sobre o DOI: 10.1007/978-1-4939-0805-9_8 (Fonte: oaDOI API)
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