ArticleThe annals of applied statistics2012
Network Inference and Biological Dynamics.
Article in The annals of applied statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Tensor network-based gene regulatory network inference for single-cell transcriptomic data.iScience · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Evolution of the Cell-Cycle Clock Models: Biochemical or Transcriptional?Advances in experimental medicine and biology · 2026Review
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- ShinyDegSEM: an interactive application for pathway perturbation analysis in gene expression studiesPeerJ · 2025Article
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.PLoS computational biology · 2024Article
- Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models.Frontiers in bioinformatics · 2022Article
- A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.Briefings in bioinformatics · 2021Article
- Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics.Current opinion in systems biology · 2021Article
- Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.Frontiers in bioinformatics · 2021Article
- Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.IEEE journal of biomedical and health informatics · 2020Article
- Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters.Bioinformatics (Oxford, England) · 2020Article
- Reverse engineering gene networks using global-local shrinkage rules.Interface focus · 2020Article
- Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants.Bioinformatics (Oxford, England) · 2019Article
- Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data.Essays in biochemistry · 2018Review
- dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.Scientific reports · 2018Article
- Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies.Nature communications · 2018Article
- Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.Cell systems · 2017Article
- Gene Network Reconstruction using Global-Local Shrinkage Priors.The annals of applied statistics · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a general formulation, we also explore the relationship between single-cell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners and suggestions for experimental design.
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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.