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  • ISSN: 2374-0116
    Early Online
    Volume 5, Issue 1
    Research Article
    Yessica Y Fermin R and Katja Ickstadt*
    A cellular function emerges from a collective action of a large number of proteins interacting and affecting each other. A major challenge in the recognition of protein interaction networks is the cell-to-cell heterogeneity within a sample. This heterogeneity hampers the usage of single parametric models that cannot handle population mixtures, such as Bayesian networks, artificial neural networks, and differential equations. A nonparametric alternative is proposed by [1] in 2011, the nonparametric Bayesian network method. An extension of the nonparametric Bayesian network method is here presented by using Gaussian dynamic Bayesian networks. This allows the possibility of an analysis considering both cell-to-cell variability and temporal correlations between interacting proteins. In our results, we show that our new method called nonparametric dynamic Bayesian network method significantly improves the nonparametric Bayesian network method for the analysis of protein time series and its results are consistent.
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