ReviewPLoS computational biology2023
From time-series transcriptomics to gene regulatory networks: A review on inference methods.
Review in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Hybrid origin and phenotype evolution of the modern maize.Journal of integrative plant biology · 2026Review
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Systemic Drug Effects in Vortioxetine-Induced Time-Series Datasets.International journal of molecular sciences · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- DC-FusionGNN: A Dual-Channel Framework Integrating Global Self-Attention and Local Topology Learning for Identifying Key Resistance Genes AgainstPlants (Basel, Switzerland) · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models.NPJ systems biology and applications · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- CTAS: a network control theory-based approach to identify key regulatory TFs of AS events during epithelial-mesenchymal transition.Briefings in bioinformatics · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell trAnscriptomics.Bioinformatics advances · 2026Article
- Dynamic Gene Network Alterations and Identification of Key Genes in the Spleen During African Swine Fever Virus (ASFV) Infection.Life (Basel, Switzerland) · 2025Article
- Inferring the regulation dynamics of oscillatory networks from scRNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- GRiNS: a python library for simulating gene regulatory network dynamics.BMC bioinformatics · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Inference and prediction for stochastic models of biological populations undergoing migration and proliferation.Journal of the Royal Society, Interface · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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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.