Genomics entails the study of large sets of genes with the goal of understanding collective gene function, rather than just that of individual genes. Genomic signal processing (GSP) is the engineering discipline that studies the processing of genomic signals. Since regulatory decisions within the cell utilize numerous inputs, analytical tools are necessary to model the multivariate influences on decision-making produced by complex genetic networks. Genomic signals must be processed to characterize their regulatory effects and their relationship to changes at both the genotypic and phenotypic levels. The aim of GSP is to integrate the theory and methods of signal processing with the global understanding of genomics, placing special emphasis on genomic regulation. GSP encompasses various methodologies related to signal profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks. In this article, we give an overview of GSP and describe how pattern recognition and network analysis are central to diagnosis and therapy for genetic diseases.