Modeling T-cell activation using gene expression profiling and state-space models

@article{Rangel2004ModelingTA,
  title={Modeling T-cell activation using gene expression profiling and state-space models},
  author={Claudia Rangel and John Angus and Zoubin Ghahramani and Maria Lioumi and Elizabeth Sotheran and Alessia Gaiba and David L. Wild and Francesco Falciani},
  journal={Bioinformatics},
  year={2004},
  volume={20 9},
  pages={1361-72}
}
MOTIVATION We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling… CONTINUE READING
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Using Bayesian Networks to Analyze Expression Data

Journal of Computational Biology • 2000
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