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. 2014 Apr 17;15:113. doi: 10.1186/1471-2105-15-113

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Copyright © 2014 Parkkinen and Kaski; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Probabilistic connectivity mapping outperforms earlier alternatives in retrieving similar drugs. The retrieval performance is indicated with two goodness measures (top row: partial AUC, bottom row: top-10 MAP) and two ground truths (left: ATC codes, right: Tanimoto similarity of the 2D fingerprints of the drugs). Probabilistic connectivity mapping (purple color) is performed with three models: Group Factor Analysis (GFA), sparse factor analysis (sFA), and Bayesian PCA (BPCA). These are compared to two earlier connectivity mapping methods (orange color): rank-based average enrichment-score distance (AESD) and the Pearson correlation over the differential expression profiles (COR).