title: Short time-series microarray analysis: methods and challenges.
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.
authors.affiliation: Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA. xwang@egr.msu.edu
authors.forename: Xuewei
authors.initials: X
authors.lastname: Wang
authors.forename: Ming
authors.initials: M
authors.lastname: Wu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Li
authors.forename: Christina
authors.initials: C
authors.lastname: Chan
mesh_terms: D005786:Gene Expression Regulation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D013997:Time Factors