ArticleBMC systems biology2008
Short time-series microarray analysis: methods and challenges.
Article in BMC systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Time-series analysis of gene expression identifies of FKBP4 as a novel protective factor in severe acute pancreatitis-associated myocardial injury.Biochemistry and biophysics reports · 2026Article
- Temporal classification of short time series data.BMC bioinformatics · 2024Article
- A personalised approach for identifying disease-relevant pathways in heterogeneous diseases.NPJ systems biology and applications · 2020Article
- Cell signaling as a cognitive process.The EMBO journal · 2017Review
- A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.Scientific reports · 2016Article
- Detecting causality from nonlinear dynamics with short-term time series.Scientific reports · 2014Article
- How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis.BMC bioinformatics · 2013Article
- Towards human-computer synergetic analysis of large-scale biological data.BMC bioinformatics · 2013Article
- Transcriptional response to foraging experience in the honey bee mushroom bodies.Developmental neurobiology · 2012Article
- Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study.BMC bioinformatics · 2011Article
- Apomictic and sexual ovules of Boechera display heterochronic global gene expression patterns.The Plant cell · 2010Article
- A reliable measure of similarity based on dependency for short time series: an application to gene expression networks.BMC bioinformatics · 2009Article
- Gene expression during Drosophila melanogaster egg development before and after reproductive diapause.BMC genomics · 2009Article
- The role of predictive modelling in rationally re-engineering biological systems.Nature reviews. Microbiology · 2009Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this 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.