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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Book ChapterDOI
Mining time-delayed coherent patterns in time series gene expression data
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Dissertation
Analysis of regulated genes of BMP4 signaling in breast cancer cell lines
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Proceedings ArticleDOI
Discovery of Versatile Temporal Subspace Patterns in 3-D Datasets
Zhen Hu,Raj K. Bhatnagar +1 more
TL;DR: This paper presents an algorithm for searching for multiple contiguous temporal subintervals within which the selected objects demonstrate existence of clear patterns and demonstrates the power and advantages of this algorithm by using a synthetic dataset and a pharmacokinetics dataset for which other researchers have recently published results.
References
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