Short time-series microarray analysis: Methods and challenges

Xuewei Wang, Ming Wu, Zheng Li, Christina Chan

Research output: Contribution to journalArticle

  • 28 Citations

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.

LanguageEnglish (US)
Article number58
JournalBMC Systems Biology
Volume2
DOIs
StatePublished - Jul 7 2008

Profile

Microarray Analysis
Time Series Analysis
Microarrays
Time Series Data
Time series
Microarray Data
Microarray
Computational Methods
Simplification
Gene Expression
Time series analysis
Computational methods
Gene expression
Sampling

ASJC Scopus subject areas

  • Molecular Biology
  • Structural Biology
  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Short time-series microarray analysis : Methods and challenges. / Wang, Xuewei; Wu, Ming; Li, Zheng; Chan, Christina.

In: BMC Systems Biology, Vol. 2, 58, 07.07.2008.

Research output: Contribution to journalArticle

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