Discovery of the relations between genetic polymorphism and adverse drug reactions

The genetic polymorphism of Cytochrome P450 (CYP 450) is considered as one of the main causes for adverse drug reactions (ADRs). In order to explore the latent correlations between ADRs and the genetic polymorphism, a new model is proposed in which both the inputs of the genetic locuses (i.e.CYP2D6*2, CYP2D6*10, CYP2D6*14, CYP1A2*1C and CYP1A2*1F) and occurrence as probabilistic distribution. A generative model is proposed to describe the joint distributions of occurrence of ADRs and the diversity of genetic sub-types of the input variables. The new algorithm is developed based on Generative Stochastic Networks (GSN) model. A Markov chain from a training data set is applied for the learning as a transition operator to simulate a probabilistic distribution. The transition distribution is conditional on the previous step of the chain thus it is able to perform learning at a much lower cost than the conventional maximal likelihood method. The experiment results show that the newly algorithm is more effective than the available conventional methods.