Deep learning for epigenomic data analysis has demonstrated to be very promising for analysis of genomic and epigenomic data. In this paper we introduce the use of Recursive Convolutional Neural Networks (RCNN) as tool for epigenomic data analysis. We focus on the task of predicting gene expression from the intensity of histone modifications. The proposed RCNN architecture can be applied on data of an arbitrary size and has a single meta-parameter that quantifies the models capacity making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems while having several orders of magnitude fewer parameters.