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Summary

(see mhc-nips11-v2). Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011

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CC0
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bioinformatics mhc UCB
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    Format not HDF5, zip or tar archive. 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Description

For some reason, the bz2 archive has been corrupted. Please look at: https://mldata.org/repository/data/viewslug/mhc-nips11-v2/

Data used in experiment in: Andreas Krause, Cheng Soon Ong. Contextual Gaussian Process Bandit Optimization. Advances in Neural Information Processing, 2011.

URLs
https://mldata.org/repository/data/viewslug/mhc-nips11-v2/
Publications
    Data Source
    B. Peters et. al. A community resource benchmarking predictions of peptide binding to mhc-i molecules. PLoS Computational Biology, 2(6):e65, 2006.
    Measurement Details

    Output kernel matrices (task similarity) obtained from

    C. Widmer, N. Toussaint, Y. Altun, and G. Raetsch. Inferring latent task structure for multitask learning by multiple kernel learning. BMC Bioinformatics, 11(Suppl 8:S5), 2010.

    all_tasks = ['A_2403', 'A_2402', 'A_2301', 'A_0201', 'A_0203', 'A_0202', 'A_6901'] Ky = array([[1., 0.9, 0.85, 0.25, 0., 0.3, 0.], [0.9, 1., 0.9, 0., 0., 0.25, 0.], [0.85, 0.9, 1., 0.25, 0., 0.25, 0.], [0.25, 0., 0.25, 1., 0.85, 0.85, 0.55], [0., 0., 0., 0.85, 1., 0.85, 0.45], [0.3, 0.25, 0.25, 0.85, 0.85, 1., 0.45], [0., 0., 0., 0.55, 0.45, 0.45, 1.]])

    Usage Scenario

    Data conversion

    • 1st data point from split 0, and all data from split 2,3,4.
    • Features are the 45 PCA values of encoded peptides (divided by the vector norm)
    • Labels are -log10(ic50s)+log10(500)
    • First column contains the label, second column the context, the rest the features

    Experimental notes

    • A base GP is trained on the first example
    • GP UCB run on all the rest
    revision 1
    by cong on 2014-12-06 07:20
    revision 2
    by cong on 2016-01-07 02:27

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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