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We developed a data-mining and visualization approach to analyze the mode-of-action (MOA) of a set of drugs. Starting from wide-genome expression data following perturbations with different compounds in a reference data-set, our method realizes an euclidean embedding providing a map of MOAs in which drugs sharing the therapeutic application or a subset of molecular targets lies in close positions. First we build a low-dimensional, visualizable space combining a rank-aggregation method and a recent tool for the analysis of the enrichment of a set of genes in ranked lists (based on the Kolmogorov-Smirnov statistic). This space is obtained using prior knowledge about the data-set composition but with no assumptions about the similarities between different drugs. Then we assess that, despite the complexity and the variety of the experimental conditions, our aim is reached with good performance without across-condition normalization procedures.