Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.

Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators as well as proteins and enzymes shaping the DNA template. Such intrinsic complexity makes uncovering connections difficult and it calls for corresp...

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Autores principales:	Hossein Zare, Mostafa Kaveh, Arkady Khodursky
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a1fedea4c5ed47e2a36158ea53a197b6
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spelling	oai:doaj.org-article:a1fedea4c5ed47e2a36158ea53a197b62021-11-18T06:48:09ZInferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.1932-620310.1371/journal.pone.0021969https://doaj.org/article/a1fedea4c5ed47e2a36158ea53a197b62011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21857910/?tool=EBIhttps://doaj.org/toc/1932-6203Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators as well as proteins and enzymes shaping the DNA template. Such intrinsic complexity makes uncovering connections difficult and it calls for corresponding methodologies, which are adapted to the available data. Here we present a new computational method that predicts interactions between transcription factors and target genes using compendia of microarray gene expression data and documented interactions between genes and transcription factors. The proposed method, called Kernel Embedding of Regulatory Networks (KEREN), is based on the concept of gene-regulon association, and captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct transcription regulatory interactions on a genome-wide scale in the model bacteria Escherichia coli (E. coli). Application of the method not only yielded accurate predictions of verifiable interactions, which outperformed on certain metrics comparable methodologies, but also demonstrated the utility of a geometric approach in the analysis of high-dimensional biological data. We also described possible applications of kernel embedding techniques to other function and network discovery algorithms.Hossein ZareMostafa KavehArkady KhodurskyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e21969 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hossein Zare Mostafa Kaveh Arkady Khodursky Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
description	Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators as well as proteins and enzymes shaping the DNA template. Such intrinsic complexity makes uncovering connections difficult and it calls for corresponding methodologies, which are adapted to the available data. Here we present a new computational method that predicts interactions between transcription factors and target genes using compendia of microarray gene expression data and documented interactions between genes and transcription factors. The proposed method, called Kernel Embedding of Regulatory Networks (KEREN), is based on the concept of gene-regulon association, and captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct transcription regulatory interactions on a genome-wide scale in the model bacteria Escherichia coli (E. coli). Application of the method not only yielded accurate predictions of verifiable interactions, which outperformed on certain metrics comparable methodologies, but also demonstrated the utility of a geometric approach in the analysis of high-dimensional biological data. We also described possible applications of kernel embedding techniques to other function and network discovery algorithms.
format	article
author	Hossein Zare Mostafa Kaveh Arkady Khodursky
author_facet	Hossein Zare Mostafa Kaveh Arkady Khodursky
author_sort	Hossein Zare
title	Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
title_short	Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
title_full	Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
title_fullStr	Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
title_full_unstemmed	Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
title_sort	inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/a1fedea4c5ed47e2a36158ea53a197b6
work_keys_str_mv	AT hosseinzare inferringatranscriptionalregulatorynetworkfromgeneexpressiondatausingnonlinearmanifoldembedding AT mostafakaveh inferringatranscriptionalregulatorynetworkfromgeneexpressiondatausingnonlinearmanifoldembedding AT arkadykhodursky inferringatranscriptionalregulatorynetworkfromgeneexpressiondatausingnonlinearmanifoldembedding
_version_	1718424370892767232

Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.

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