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. © 2008 Wang et al; licensee BioMed Central Ltd.
CITATION STYLE
Wang, X., Wu, M., Li, Z., & Chan, C. (2008, July 7). Short time-series microarray analysis: Methods and challenges. BMC Systems Biology. https://doi.org/10.1186/1752-0509-2-58
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