ReviewGenes2021
Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data.
Review in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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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.
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Who cites it
16 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Finetuning Foundation Models for Temporal Clinical Transcriptomics Data.Bioinformatics (Oxford, England) · 2026Article
- How Polyphenol Metabolites Spatiotemporally Reprogram Transcription Factors and Human Proteostasis: A Metabolite-Centric Framework.Current issues in molecular biology · 2026Review
- Dynamic Alterations in the Blood Transcriptome Characterize Drug Use Behavior and Co-Morbidities in Cocaine Use Disorder: A Preliminary Study.The European journal of neuroscience · 2026Article
- Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure.Archives of toxicology · 2025Article
- Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data.Stats · 2025Article
- Temporal dynamics of gene and protein signatures following volumetric muscle loss.Frontiers in cell and developmental biology · 2025Article
- Unveiling gene regulatory networks during cellular state transitions without linkage across time points.Scientific reports · 2024Article
- Circadian transcriptome processing and analysis: a workflow for muscle stem cells.FEBS open bio · 2023Article
- Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics.Nature communications · 2023Article
- Time-Series Transcriptomic Analysis of Contrasting Rice Materials under Heat Stress Reveals a Faster Response in the Tolerant Cultivar.International journal of molecular sciences · 2023Article
- Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover.Proteomics · 2023Article
- Transcriptomic thermal plasticity underlying gonadal development in a turtle with ZZ/ZW sex chromosomes despite canalized genotypic sex determination.Ecology and evolution · 2023Article
- A simple guide to de novo transcriptome assembly and annotation.Briefings in bioinformatics · 2022Review
- Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China.Frontiers in medicine · 2022Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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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.