Short Time-Series Microarray Analysis: Methods and Challenges
Date
2008-7-7
Authors
Wang, Xuewei
Wu, Ming
Li, Zheng
Chan, Christina
Version
OA Version
Citation
Wang, Xuewei, Ming Wu, Zheng Li, Christina Chan. "Short time-series microarray analysis: Methods and challenges" BMC Systems Biology 2:58. (2008)
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.
Description
License
Copyright 2008 Wang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.