The aim of this thesis is to perform an integrative analysis of gene expression and connectivity data using machine learning techniques. The implemented algorithm is able to detect the physical connection’s degree between brain areas exploiting patterns of gene expression profiles. In this study, I used gene expression and connectivity data (axonal projections) available on the Allen Mouse Brain Atlas (AMBA) resources combined with the connection’s intensity reported on Brain Architecture Management System (BAMS) database. When the viral tracer is injected in a brain region, called source region, it produces axonal projections in several target regions. Expression and connectivity data are provided as grid data, a 3D matrix representing mouse brain volume.