Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data

Antti Honkela, Magnus Rattray, Neil D. Lawrence
, Springer-Verlag , 2012.

Abstract

Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.

Cite this Paper

BibTeX
@Misc{Honkela:mining12, title = {Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data}, author = {Honkela, Antti and Rattray, Magnus and Lawrence, Neil D.}, year = {2012}, editor = {Mamitsuka, Hiroshi and DeLisi, Charles and Kanehisa, Minoru}, publisher = {Springer-Verlag}, url = {http://inverseprobability.com/publications/honkela-mining12.html}, abstract = {Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor. } }
Endnote
%0 Generic %T Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data %A Antti Honkela %A Magnus Rattray %A Neil D. Lawrence %D 2012 %E Hiroshi Mamitsuka %E Charles DeLisi %E Minoru Kanehisa %F Honkela:mining12 %I Springer-Verlag %U http://inverseprobability.com/publications/honkela-mining12.html %X Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.
RIS
TY - GEN TI - Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data AU - Antti Honkela AU - Magnus Rattray AU - Neil D. Lawrence BT - Data Mining for Systems Biology DA - 2012/09/08 ED - Hiroshi Mamitsuka ED - Charles DeLisi ED - Minoru Kanehisa ID - Honkela:mining12 PB - Springer-Verlag UR - http://inverseprobability.com/publications/honkela-mining12.html AB - Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor. ER -
APA
Honkela, A., Rattray, M. & Lawrence, N.D.. (2012). Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data. Available from http://inverseprobability.com/publications/honkela-mining12.html.

Related Material