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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Proceedings ArticleDOI
Discovering Temporal Associations among Significant Changes in Gene Expression
TL;DR: A framework to mine associations among significant changes in multivariate time-series data is introduced and a propositional confirmation-guided rule discovery method is used to discover associations among these significant changes.
Book ChapterDOI
Clustering change patterns using Fourier transformation with time-course gene expression data.
TL;DR: A statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression and it showed that, as the method clusters with the probability-neighboring data, the model-based clustering with the proposed model yielded biologically interpretable results.
Journal ArticleDOI
Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.
TL;DR: This work evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them and examined the relationship between the pole placement of the inferred system and the inference performance.
Proceedings ArticleDOI
Synthetic Time Series Resembling Human (HeLa) Cell-Cycle Gene Expression Data and Application to Gene Regulatory Network Discovery
TL;DR: A new method is proposed that generates synthetic data resembling human (HeLa) cell-cycle gene expression data that indicates Granger causality (GC) methods substantially outperform Pearson correlation coefficient (PCC) while time-shifted PCC can give comparable performance as GC methods.
Proceedings ArticleDOI
Integrate Qualitative Biological Knowledge to Build Gene Networks by Parallel Dynamic Bayesian Network Structure Learning
TL;DR: A software system targeting on building large-scale gene networks realized by both parallel computing, and a novel data integration model which fuses qualitative gene interaction information with quantitative microarray data under the Dynamic Bayesian Networks framework are introduced.
References
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