Abstract In this chapter, we show the application of deep neural networks (DNNs) in phenotype prediction problems for the interpretation of microarray data from disease patients and healthy controls to better understand the genetic pathways that are involved. These problems have a very underdetermined character since the number of control variables (monitored genetic probes) is always much larger than the number of observed data (control and disease samples). Therefore, the aim of these methods should be to perform the sampling of their uncertainty space, which is composed of the set of genetic signatures that predict the phenotype with similar predictive accuracy in order to establish the genetic pathways altered by the disease. The methodology presented in this chapter samples the uncertainty space of the phenotype prediction problem via DNN with a prior probability induced by the prior discriminatory power of each individual gene as measured by their Fisher’s ratio, established within the set of differentially expressed genes. The DNN serves to evaluate the posterior predictive accuracy of the genetic networks that are sampled. The high discriminatory genetic networks sampled in the uncertainty space serve to understand the causes of disease development and finding new therapeutic targets and performing drug repositioning. We show the application to a microarray dataset associated with a specific rare disease called inclusion body myositis (IBM).