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An extension of Independent Component Analysis (ICA) to the situation when the mixture of signals is contaminated by multiplicative noise is proposed in this paper. The ICA methods search for the most independent output after a linear transformation of the data vector. If the ICA model is followed by these data, the result of this search is the inverse of the unknown mixture. On the other hand, if...
We have revised the Markov model used in the analysis of microarray time-series data to uncover the gene regulatory network. Previous linear models establishes genetic relations between the microarray data which are assumed to have noise. We propose a new model to distinguish between observed data and real expression levels. The new model does not overestimate the noise and fits better the nature...
We have revised the Markov lineal model used in the analysis of microarray time-series data. According to this model, the expression level of a given gene at any specific time is a linear combination of the measured expression levels of other genes at previous time instants, plus noise. The problem of uncovering such relationships can be solved using variational Bayesian methods. The linear model...
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