Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang

Producción científica: Conference contribution

1 Cita (Scopus)

Resumen

Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.

Idioma originalEnglish (US)
Título de la publicación alojada2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Páginas361-364
Número de páginas4
DOI
EstadoPublished - 2011
Evento2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duración: jun 28 2011jun 30 2011

Serie de la publicación

NombreIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011
País/TerritorioFrance
CiudadNice
Período6/28/116/30/11

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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