To validate the candidate disease genes identified from high-throughput genomic studies, a necessary step is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease-causative genes are incomplete. This article introduces a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. A learning framework is proposed to maximize the coherence between the predicted phenotype-gene set relations and the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation and two variants are designed to find the optimal solution to the objective functions of the learning framework.