Short Time-Series Microarray Analysis: Methods and Challenges
|dc.identifier.citation||Wang, Xuewei, Ming Wu, Zheng Li, Christina Chan. "Short time-series microarray analysis: Methods and challenges" BMC Systems Biology 2:58. (2008)|
|dc.description.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.||en_US|
|dc.description.sponsorship||National Institute of Health (IR01GM079688-01); National Science Foundation (BES 0425821); MSU Foundation on the Center for Systems Biology||en_US|
|dc.rights||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.||en_US|
|dc.title||Short Time-Series Microarray Analysis: Methods and Challenges||en_US|
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Except where otherwise noted, this item's license is described as 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.