Uncover transcription factor mediated gene regulations using Bayesian nonnegative factor models

Jia Meng, Hung I. Chen, Jianqiu Zhang, Yidong Chen, Yufei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 analysis 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; further more, we show that the approach can be slightly altered to include miRNA regulations as well. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through artificial and real data.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011
DOIs
StatePublished - Nov 17 2011
Event2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011 - Xi'an, China
Duration: Sep 14 2011Sep 16 2011

Publication series

Name2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011

Other

Other2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011
CountryChina
CityXi'an
Period9/14/119/16/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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