Research Article
Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality
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@INPROCEEDINGS{10.1007/978-3-642-02466-5_17, author={Qiang Luo and Xu Liu and Dongyun Yi}, title={Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality}, proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1}, proceedings_a={COMPLEX PART 1}, year={2012}, month={5}, keywords={partial Granger causality gene regulatory networks time series data projection pursuit}, doi={10.1007/978-3-642-02466-5_17} }- Qiang Luo
Xu Liu
Dongyun Yi
Year: 2012
Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_17
Abstract
Reconstructing gene network structure from Microarray time-series data is a basic problem in Systems Biology. In gene regulation networks, the time delays and the combination effects which are not considered by most existent models are key factors to understand the genetic regulatory networks. To address these problems, this paper proposed a fast algorithm to learn initial network structures for gene networks from time-series data by employing the Granger causality model to analyze the time delays and the combination effects for gene regulation. The simulation results on a synthetic network and the ethylene pathway in show that the proposed algorithm is a promise tool for learning network structures from time-series data.
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