Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Probabilistic Approximations of Signaling Pathway Dynamics
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We propose a method to build gene regulatory networks (GRN) capable of representing time-delayed regulations. The gene expression data is represented in two types of graphical models: a linear model using a dynamic Bayesian network (DBN) and a skip model using a hidden Markov model. The linear model is designed to find short-delays and skip model for long-delays. The algorithm was tested on time-series data obtained on yeast cell-cycle and validated against protein-protein interaction data. The proposed method better fits expression profiles compared to classical higher-order DBN and found core genes that are crucial in cell-cycle regulation.