Analyzing time series gene expression data
TLDR
This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.Abstract:
Motivation: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates).
Results: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.read more
Citations
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Journal ArticleDOI
TriGen: A genetic algorithm to mine triclusters in temporal gene expression data
TL;DR: The TriGen algorithm is presented, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously, and has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times.
Journal ArticleDOI
Inferring pairwise regulatory relationships from multiple time series datasets
TL;DR: A new computational model and an associated algorithm are presented to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales and achieves a much lower false-positive rate.
Journal ArticleDOI
Comparative developmental expression profiling of two C. elegans isolates.
TL;DR: This work used microarrays that comprehensively cover known and predicted worm genes to compare the landscape of genetic variation over developmental time between two isolates of C. elegans to identify several novel motifs that appear to play a role in regulating gene expression during development, and proposes functional annotations for many previously unannotated genes.
Journal ArticleDOI
Combined static and dynamic analysis for determining the quality of time-series expression profiles
TL;DR: It is shown that by combining synchronized and unsynchronized human cell cycle data, the algorithm correctly distinguishes cycling genes from genes that specifically react to an environmental stimulus even if they share similar temporal expression profiles.
Proceedings ArticleDOI
Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures
TL;DR: This study comprehensively evaluates 71 time-series distance measures and debunk four long-standing misconceptions that significantly alter the landscape of what is known about existing distance measures.
References
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