Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections
Authors
Keywords
-
Journal
Statistical Applications in Genetics and Molecular Biology
Volume 20, Issue 4-6, Pages 121-143
Publisher
Walter de Gruyter GmbH
Online
2021-12-28
DOI
10.1515/sagmb-2020-0054
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem
- (2020) Ming-Ju Tsai et al. BIOINFORMATICS
- PFBNet: a priori-fused boosting method for gene regulatory network inference
- (2020) Dandan Che et al. BMC BIOINFORMATICS
- Gene regulatory network inference from sparsely sampled noisy data
- (2020) Atte Aalto et al. Nature Communications
- dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
- (2018) Vân Anh Huynh-Thu et al. Scientific Reports
- Inferring gene expression networks with hubs using a degree weighted Lasso approach
- (2018) Nurgazy Sulaimanov et al. BIOINFORMATICS
- Orchestrating high-throughput genomic analysis with Bioconductor
- (2015) Wolfgang Huber et al. NATURE METHODS
- RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond
- (2015) Socorro Gama-Castro et al. NUCLEIC ACIDS RESEARCH
- Learning Bayesian Networks with thebnlearnRPackage
- (2015) Marco Scutari Journal of Statistical Software
- SANTA: Quantifying the Functional Content of Molecular Networks
- (2014) Alex J. Cornish et al. PLoS Computational Biology
- Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues
- (2013) George Michailidis et al. MATHEMATICAL BIOSCIENCES
- Integrating external biological knowledge in the construction of regulatory networks from time-series expression data
- (2012) Kenneth Lo et al. BMC Systems Biology
- Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination
- (2012) Matthias Böck et al. PLoS One
- How to infer gene networks from expression profiles, revisited
- (2011) C. A. Penfold et al. Interface Focus
- Inferring gene regression networks with model trees
- (2010) Isabel A Nepomuceno-Chamorro et al. BMC BIOINFORMATICS
- Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
- (2010) Vân Anh Huynh-Thu et al. PLoS One
- Weighted-LASSO for Structured Network Inference from Time Course Data
- (2010) Camille Charbonnier et al. Statistical Applications in Genetics and Molecular Biology
- Regularized estimation of large-scale gene association networks using graphical Gaussian models
- (2009) Nicole Krämer et al. BMC BIOINFORMATICS
- Recursive regularization for inferring gene networks from time-course gene expression profiles
- (2009) Teppei Shimamura et al. BMC Systems Biology
- Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods
- (2009) Daniel Marbach et al. JOURNAL OF COMPUTATIONAL BIOLOGY
- Gene regulatory network inference: Data integration in dynamic models—A review
- (2008) Michael Hecker et al. BIOSYSTEMS
- minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
- (2008) Patrick E Meyer et al. BMC BIOINFORMATICS
- WGCNA: an R package for weighted correlation network analysis
- (2008) Peter Langfelder et al. BMC BIOINFORMATICS
- Rank-based edge reconstruction for scale-free genetic regulatory networks
- (2008) Guanrao Chen et al. BMC BIOINFORMATICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started