ArticleThe annals of applied statistics2010
Sparse Regulatory Networks.
Article in The annals of applied statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 15 citations in OpenAlex.
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
- SurvGME: an R package for survival analysis with graphical and measurement error models.BMC bioinformatics · 2026Article
- Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.bioRxiv : the preprint server for biology · 2026Article
- GUEST: an R package for handling estimation of graphical structure and multiclassification for error-prone gene expression data.Bioinformatics (Oxford, England) · 2024Article
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.BMC bioinformatics · 2022Article
- Estimating and Identifying Unspecified Correlation Structure for Longitudinal Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2015Article
- Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.IET systems biology · 2014Article
- A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.Frontiers in bioengineering and biotechnology · 2014Review
- Robust Gaussian graphical modeling via l1 penalization.Biometrics · 2012Article
- Integrating external biological knowledge in the construction of regulatory networks from time-series expression data.BMC systems biology · 2012Article
- Construction of regulatory networks using expression time-series data of a genotyped population.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
In many organisms the expression levels of each gene are controlled by the activation levels of known "Transcription Factors" (TF). A problem of considerable interest is that of estimating the "Transcription Regulation Networks" (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L(1) penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates.
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.