Enrichment Map is a network-based visualization method for gene-set enrichment results, developed to address gene-set redundancy and improve the interpretation of large gene lists. The method organizes gene-sets into a network where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into clusters, enabling users to quickly identify major enriched functional themes and interpret results more effectively. Enrichment Map is implemented as a freely available and user-friendly plug-in for Cytoscape, allowing any research project generating gene lists to benefit from this visualization framework. Gene-set enrichment analysis identifies functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software work against this ideal. Enrichment Map addresses this by visualizing gene-set enrichment results in a network, where nodes represent gene-sets and edges represent gene overlap. Node color encodes the enrichment score, and node size represents the number of genes in the gene-set. Edge thickness is proportional to the overlap between gene-sets, calculated using the Jaccard or overlap coefficients. Enrichment Map can be used to analyze gene-expression experiments, such as estrogen treatment of breast cancer cells and early-onset colon cancer. In the estrogen treatment example, gene-sets were analyzed for enrichment significance using GSEA and organized into a network. The network was visualized with node color representing enrichment significance, node size representing gene-set size, and edge thickness representing gene-set overlap. In the early-onset colon cancer example, gene expression data were scored for differential expression and used to generate enrichment results, which were then visualized using Enrichment Map. Enrichment Map is compatible with any type of enrichment test or gene-set source, including disease gene lists and pathway databases. It can be used to compare enrichment results from different experiments and to identify functional groups that differ between experiments. The visualization framework is essential for this, as traditional displays of enrichment require tedious and error-prone navigation of flat tables, often resulting in investigators choosing only a handful of gene-sets for follow-up out of the thousands available to them in a genomics experiment. The heat map view in Enrichment Map enables the user to zoom in and explore an enriched gene-set in more detail and the query set analysis facilitates exploration of relations to known disease genes or regulatory modules.Enrichment Map is a network-based visualization method for gene-set enrichment results, developed to address gene-set redundancy and improve the interpretation of large gene lists. The method organizes gene-sets into a network where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into clusters, enabling users to quickly identify major enriched functional themes and interpret results more effectively. Enrichment Map is implemented as a freely available and user-friendly plug-in for Cytoscape, allowing any research project generating gene lists to benefit from this visualization framework. Gene-set enrichment analysis identifies functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software work against this ideal. Enrichment Map addresses this by visualizing gene-set enrichment results in a network, where nodes represent gene-sets and edges represent gene overlap. Node color encodes the enrichment score, and node size represents the number of genes in the gene-set. Edge thickness is proportional to the overlap between gene-sets, calculated using the Jaccard or overlap coefficients. Enrichment Map can be used to analyze gene-expression experiments, such as estrogen treatment of breast cancer cells and early-onset colon cancer. In the estrogen treatment example, gene-sets were analyzed for enrichment significance using GSEA and organized into a network. The network was visualized with node color representing enrichment significance, node size representing gene-set size, and edge thickness representing gene-set overlap. In the early-onset colon cancer example, gene expression data were scored for differential expression and used to generate enrichment results, which were then visualized using Enrichment Map. Enrichment Map is compatible with any type of enrichment test or gene-set source, including disease gene lists and pathway databases. It can be used to compare enrichment results from different experiments and to identify functional groups that differ between experiments. The visualization framework is essential for this, as traditional displays of enrichment require tedious and error-prone navigation of flat tables, often resulting in investigators choosing only a handful of gene-sets for follow-up out of the thousands available to them in a genomics experiment. The heat map view in Enrichment Map enables the user to zoom in and explore an enriched gene-set in more detail and the query set analysis facilitates exploration of relations to known disease genes or regulatory modules.