SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Franceschini, A., Lin, J., von Mering, C., & Jensen, L., J. Bioinformatics, 32(7):1085-1087, 4, 2016.
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Paper Website doi abstract bibtex A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods.\n\nAVAILABILITY AND IMPLEMENTATION: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy CONTACT: lars.juhl.jensen@cpr.ku.dk.
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abstract = {A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods.\n\nAVAILABILITY AND IMPLEMENTATION: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy CONTACT: lars.juhl.jensen@cpr.ku.dk.},
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