Complex genetic systems may be modelled using causal graphical models. Microarrays may provide information only after normalizing raw data, for example, using graphical models. Probabilistic Causal models are therefore a natural methodological framework to handle both measurement error and causal information. In this talk this methodology is presented by using several illustrative examples.
Statistical Methods in Genetics and Molecular Biology / F. M. Stefanini. - STAMPA. - SIGA 2003 Congress acta:(2005), pp. 10-10. ( XLIX Convegno Annuale della SSocietà di Genetica Agraria Potenza settembre 2005).
Statistical Methods in Genetics and Molecular Biology
STEFANINI, FEDERICO MATTIA
2005
Abstract
Complex genetic systems may be modelled using causal graphical models. Microarrays may provide information only after normalizing raw data, for example, using graphical models. Probabilistic Causal models are therefore a natural methodological framework to handle both measurement error and causal information. In this talk this methodology is presented by using several illustrative examples.
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