Characterizing Gene Expression Time Series using a Hidden Markov Model

Part of the Computational Biology book series (COBO, volume 5)

We are concerned with the temporal clustering of a series of gene expression data using a hidden Markov model (HMM) and in so doing providing an intuitive way of characterizing the developmental processes within the cell. By explicitly modelling the time dependent aspects of these data using a novel form of the HMM, each stage of cell development can be depicted. In this model, the hitherto unknown development process that manifests itself as changes in gene expression is represented by hidden concepts.We use clustering to learn probabilistic descriptions of these hidden concepts in terms of a hidden Markov process. Finally, we derive linguistic identifiers from the transition matrices that characterize the developmental processes. Such identifiers could be used to annotate a genome database to assist data retrieval.


Hide Markov Model Gene Expression Data Transition Matrix Temporal Cluster Hide Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2004

Authors and Affiliations

  1. 1.School of Computing and Information Engineering, Faculty of InformaticsUniversity of UlsterNorthern Ireland

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