ArticleBioinformatics (Oxford, England)2009
Grouped graphical Granger modeling for gene expression regulatory networks discovery.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.Briefings in bioinformatics · 2024Article
- Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO.IEEE transactions on information theory · 2023Article
- Neural Granger Causality.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Granger Causality: A Review and Recent Advances.Annual review of statistics and its application · 2022Article
- Where Do We Stand in Regularization for Life Science Studies?Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Causal network inference from gene transcriptional time-series response to glucocorticoids.PLoS computational biology · 2021Article
- Detecting interaction networks in the human microbiome with conditional Granger causality.PLoS computational biology · 2019Article
- Time-lagged Ordered Lasso for network inference.BMC bioinformatics · 2018Article
- Windowed Granger causal inference strategy improves discovery of gene regulatory networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.Bioinformatics (Oxford, England) · 2017Article
- Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.Scientific reports · 2017Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.Computational and mathematical methods in medicine · 2017Article
- Inferring sparse networks for noisy transient processes.Scientific reports · 2016Article
- Quantitative Analysis of Global Proteome and Lysine Acetylome Reveal the Differential Impacts of VPA and SAHA on HL60 Cells.Scientific reports · 2016Article
- Inference of biological networks using Bi-directional Random Forest Granger causality.SpringerPlus · 2016Article
- Article
- Mining precise cause and effect rules in large time series data of socio-economic indicators.SpringerPlus · 2016Article
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4 authors.
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Abstract
We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of 'Granger causality' to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem--the group structure among the lagged temporal variables naturally imposed by the time series they belong to. Specifically, existing methods in computational biology share this shortcoming, as well as additional computational limitations, prohibiting their effective applications to the large datasets including a large number of genes and many data points. In the present article, we propose a novel methodology which we term 'grouped graphical Granger modeling method', which overcomes the limitations mentioned above by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to. We demonstrate the effectiveness of the proposed methodology on both simulated and actual gene expression data, specifically the human cancer cell (HeLa S3) cycle data. The simulation results show that the proposed methodology generally exhibits higher accuracy in recovering the underlying causal structure. Those on the gene expression data demonstrate that it leads to improved accuracy with respect to prediction of known links, and also uncovers additional causal relationships uncaptured by earlier works.
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