ReviewNature reviews. Genetics2012
Studying and modelling dynamic biological processes using time-series gene expression data.
Review in Nature reviews. Genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
254 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Integrative multi-omics analysis reveals lipid/metabolite dysregulation and temporal decoupling in disease progression.Scientific reports · 2026Article
- lagCI enables inference of temporal causal relationships from dense multi-omic time series.Briefings in bioinformatics · 2026Article
- SnRNA-seq and genome-wide CRISPR screening define the complete transcriptional trajectory and functional drivers of podocyte stress.Cellular and molecular life sciences : CMLS · 2026Article
- Temporal transcriptomics identifies early-response and infection-condition-specific modules guiding host-directed anti-EBOV therapeutics.Microbiology spectrum · 2026Article
- Identification and clustering analysis of drug-responsive temporally varying genes through high-frequency longitudinal RNA sequencing.Scientific reports · 2026Article
- Existence of Causation without Correlation in Transcriptional Networks.bioRxiv : the preprint server for biology · 2026Article
- Challenges and Advances in Bioinformatics and Computational Biology.Current issues in molecular biology · 2026Article
- Transcriptome analysis ofMicrobiology spectrum · 2026Article
- Radiation-induced network rewiring in cancer: systems-level insights into environmental radioactivity and precision oncology.Frontiers in bioinformatics · 2026Review
- DANSE: a pipeline for dynamic modelling of time-series multi-omics data.BMC bioinformatics · 2025Article
- tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data.PLoS computational biology · 2025Article
- High-resolution time-series transcriptomic and metabolomic profiling reveals the regulatory mechanism underlying salt tolerance in maize.Genome biology · 2025Article
- Nonlinear memory in cell-division dynamics across species.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Interaction of genetic variants activates latent metabolic pathways in yeast.Nature communications · 2025Article
- 4D hypercomplex-valued neural network in multivariate time series forecasting.Scientific reports · 2025Article
- Recovering time-varying networks from single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- Article
194 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.
Indexed as
Identifiers
22805708What OpenQuestion holds
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.