A Bayesian framework for building transcript concentration estimation model. The sequence based model is trained on the training data in a supervised way. Then for the validation data, group-constraints are constructed from the predicted probe responsiveness to remove deconvolution ambiguity as discussed earlier. 'Fuzzy' constraints can be considered in fitting a probe intensity model with standard errors included (see (11)). During the optimization, the exact group-constraints with no standard errors serve as an initial estimate.