ArticleBioinformatics (Oxford, England)2008
Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.
Article in Bioinformatics (Oxford, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.IEEE journal of biomedical and health informatics · 2020Article
- CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2.The Journal of clinical investigation · 2018Article
- Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.EURASIP journal on bioinformatics & systems biology · 2013Article
- Predicting tissue specific transcription factor binding sites.BMC genomics · 2013Article
- Comparison of co-expression measures: mutual information, correlation, and model based indices.BMC bioinformatics · 2012Article
- Understanding transcriptional regulation by integrative analysis of transcription factor binding data.Genome research · 2012Article
- Inferring Genetic Interactions via a Data-Driven Second Order Model.Frontiers in genetics · 2012Article
- Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.PloS one · 2011Article
- Independent component analysis: mining microarray data for fundamental human gene expression modules.Journal of biomedical informatics · 2010Article
- Article
- Knowledge-guided gene ranking by coordinative component analysis.BMC bioinformatics · 2010Article
- Uncovering transcriptional interactions via an adaptive fuzzy logic approach.BMC bioinformatics · 2009Article
- Inferring Transcriptional Interactions by the Optimal Integration of ChIP-chip and Knock-out Data.Bioinformatics and biology insights · 2009Article
- Proceedings of the 2009 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference. Introduction.BMC bioinformatics · 2009Article
- Article
- Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations.BMC systems biology · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
motivationUnraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge.
methodHere, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting).
resultsIn comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.