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Wang X, Wu M, Li Z, et al. Short time-series microarray analysis: methods and challenges. BMC Syst Biol. 2008;2:58doi: 10.1186/1752-0509-2-58.
Wang, X., Wu, M., Li, Z., & Chan, C. (2008). Short time-series microarray analysis: methods and challenges. BMC systems biology, 258. https://doi.org/10.1186/1752-0509-2-58
Wang, Xuewei, et al. "Short time-series microarray analysis: methods and challenges." BMC systems biology vol. 2 (2008): 58. doi: https://doi.org/10.1186/1752-0509-2-58
Wang X, Wu M, Li Z, Chan C. Short time-series microarray analysis: methods and challenges. BMC Syst Biol. 2008 Jul 07;2:58. doi: 10.1186/1752-0509-2-58. PMID: 18605994; PMCID: PMC2474593.
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BMC Syst Biol. 2008 Jul 07;2:58. doi: 10.1186/1752-0509-2-58.

Short time-series microarray analysis: methods and challenges.

BMC systems biology

Xuewei Wang, Ming Wu, Zheng Li, Christina Chan

Affiliations

  1. Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA. [email protected]

PMID: 18605994 PMCID: PMC2474593 DOI: 10.1186/1752-0509-2-58

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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