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As an example, the TSNI (Time Series Network Identification) algorithm used ODEs to model time series expression data subject to an external perturbation [ 53].
We have tested our method on both simulated and real time series expression data.
SPM represents a general method for constructing the regulatory networks from the time series expression data.
For A. fumigatus, such time series expression data is already available and utilized in this study.
More recently a few methods for classifying time series expression data were presented.
MaSigPro applies two generalized linear regression steps to model gene expression in time series expression data.
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Short-Time series expression miner (STEM) was used to cluster the data and provide GO-term enrichments [33].
STEM (Short Time-series Expression Miner) software (http://www.cs.cmu.edu/~jernst/stem/) was used to cluster the differentially expressed TFs and distinguish the temporal expression profiles.
Complete time-series expression data of each gene are available in GEO database (GSE21989).
We used the non-parametric clustering algorithm of STEM (Short Minerseries Expression Miner, version 1.3.7) that is specifically designed to analyze short time-series expression data [ 120].
We have tested our method on simulated and real time-series expression datasets.
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