A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data

Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes