  We review a recent trend in computational systems biology which aims at using
pattern recognition algorithms to infer the structure of large-scale biological
networks from heterogeneous genomic data. We present several strategies that
have been proposed and that lead to different pattern recognition problems and
algorithms. The strenght of these approaches is illustrated on the
reconstruction of metabolic, protein-protein and regulatory networks of model
organisms. In all cases, state-of-the-art performance is reported.
