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Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion factor model (BEFaM) has been proposed, where the loading (regulatory)...
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...
In order to determine the feasibility of performing mouth to mouth and nose resuscitation according to the established guidelines, a study was carried out on the facial dimensions of 40 children under 2 months of age (20 under 1 month and 20 aged 1 month) and the open-mouth dimensions of the 40 adults in charge of these children. Only nine adults in charge of children under 1 month of age would have...
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