ArticleBMC systems biology2014
Fast Bayesian inference for gene regulatory networks using ScanBMA.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states.PLoS computational biology · 2026Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- SIGNET: transcriptome-wide causal inference for gene regulatory networks.Scientific reports · 2023Article
- Effect of model space priors on statistical inference with model uncertainty.The New England Journal of Statistics in Data Science · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory Networks.Research square · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- Comparing methods for statistical inference with model uncertainty.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- KBoost: a new method to infer gene regulatory networks from gene expression data.Scientific reports · 2021Article
- Causal network inference from gene transcriptional time-series response to glucocorticoids.PLoS computational biology · 2021Article
- Probabilistic Graphical Models Applied to Biological Networks.Advances in experimental medicine and biology · 2021Article
- PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.PloS one · 2021Article
- PFBNet: a priori-fused boosting method for gene regulatory network inference.BMC bioinformatics · 2020Article
- 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
- Bioinformatics identification of potential genes and pathways in preeclampsia based on functional gene set enrichment analyses.Experimental and therapeutic medicine · 2019Article
- Identifying Dynamical Time Series Model Parameters from Equilibrium Samples, with Application to Gene Regulatory Networks.Statistical modelling · 2019Article
- PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation.Frontiers in plant science · 2019Article
- Parallel Algorithms for Inferring Gene Regulatory Networks: A Review.Current genomics · 2018Review
- BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data.BMC systems biology · 2018Article
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3 authors.
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Abstract
backgroundGenome-wide time-series data provide a rich set of information for discovering gene regulatory relationships. As genome-wide data for mammalian systems are being generated, it is critical to develop network inference methods that can handle tens of thousands of genes efficiently, provide a systematic framework for the integration of multiple data sources, and yield robust, accurate and compact gene-to-gene relationships.
resultsWe developed and applied ScanBMA, a Bayesian inference method that incorporates external information to improve the accuracy of the inferred network. In particular, we developed a new strategy to efficiently search the model space, applied data transformations to reduce the effect of spurious relationships, and adopted the g-prior to guide the search for candidate regulators. Our method is highly computationally efficient, thus addressing the scalability issue with network inference. The method is implemented as the ScanBMA function in the networkBMA Bioconductor software package.
conclusionsWe compared ScanBMA to other popular methods using time series yeast data as well as time-series simulated data from the DREAM competition. We found that ScanBMA produced more compact networks with a greater proportion of true positives than the competing methods. Specifically, ScanBMA generally produced more favorable areas under the Receiver-Operating Characteristic and Precision-Recall curves than other regression-based methods and mutual-information based methods. In addition, ScanBMA is competitive with other network inference methods in terms of running time.
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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.