ArticleBMC systems biology2012
Integrating external biological knowledge in the construction of regulatory networks from time-series expression data.
Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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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.
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Who cites it
29 citing papers in PubMed.
- Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states.PLoS computational biology · 2026Article
- Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- Identifying large-scale interaction atlases using probabilistic graphs and external knowledge.Journal of clinical and translational science · 2022Article
- Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data.Journal of computational biology : a journal of computational molecular cell biology · 2019Article
- Temporal genetic association and temporal genetic causality methods for dissecting complex networks.Nature communications · 2018Article
- REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES.The annals of applied statistics · 2018Article
- DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.Genes · 2018Article
- fastBMA: scalable network inference and transitive reduction.GigaScience · 2017Article
- GUIdock-VNC: using a graphical desktop sharing system to provide a browser-based interface for containerized software.GigaScience · 2017Article
- Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.PloS one · 2017Article
- F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.PloS one · 2017Article
- Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data.The annals of applied statistics · 2016Article
- BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.PloS one · 2016Article
- GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research.PloS one · 2016Article
- SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.PLoS computational biology · 2015Article
- Genome-wide identification and analysis of drought-responsive genes and microRNAs in tobacco.International journal of molecular sciences · 2015Article
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks.Source code for biology and medicine · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundInference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge.
resultsWe formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models.
conclusionsWe demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.
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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.