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References
Shen, J. et al. Proc. Natl. Acad. Sci. USA 104, 4337–4341 (2007).
Wikberg, J.E. & Mutulis, F. Nat. Rev. Drug Discov. 7, 307–323 (2008).
Yabuuchi, H. et al. Mol. Syst. Biol. 7, 472 (2011).
Bellucci, M., Agostini, F., Masin, M. & Tartaglia, G.G. Nat. Methods 8, 444–445 (2011).
Gottlieb, A., Stein, G.Y., Ruppin, E. & Sharan, R. Mol. Syst. Biol. 7, 496 (2011).
Vert, J.-P. & Yamanishi, Y. Advances in Neural Information Processing Systems vol. 17 (eds. Saul, L., Weiss. Y. & Bottou, L.) 1433–1440 (MIT Press, Cambridge, Massachusetts, USA, 2005).
Flicek, P. et al. Nucleic Acids Res. 39, D800–D806 (2011).
Schaefer, M.H. et al. PLoS ONE 7, e31826 (2012).
Tropsha, A. & Golbraikh, A. Curr. Pharm. Des. 13, 3494–3504 (2007).
Olah, M., Bologa, C. & Oprea, T.I. J. Comput. Aided Mol. Des. 18, 437–449 (2004).
Acknowledgements
We thank W.S. Noble, A. Ben-Hur, J.-P. Vert and V. Helms for stimulating discussions and critical comments on the manuscript. This work was supported by grants to E.M.M. from the US National Institutes of Health, the US Army (58343-MA), Cancer Prevention and Research in Texas and the Welch (F1515) Foundation. Y.P. acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG-Forschungsstipendium).
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Park, Y., Marcotte, E. Flaws in evaluation schemes for pair-input computational predictions. Nat Methods 9, 1134–1136 (2012). https://doi.org/10.1038/nmeth.2259
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DOI: https://doi.org/10.1038/nmeth.2259
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