Data
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mhc-affinity - submitted by cong 3 views, 4530 downloads, 0 comments
last edited by cong - Dec 6, 2014, 07:33 CET Rating
- Summary:
Binding affinity of MHC class I molecules
- License: CC0
- Tags: bioinformatics mhc
- Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
- Download: bz2 (26.5 MB)
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
Binding affinity of MHC class I molecules
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mhc-nips11 - submitted by cong 995 views, 3114 downloads, 0 comments
last edited by cong - Jan 7, 2016, 02:27 CET Rating
- Summary:
(see mhc-nips11-v2). Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011
- License: CC0
- Tags: bioinformatics mhc UCB
- Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
- Download: bz2 (4.0 MB)
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- 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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mhc-nips11-v2 - submitted by cong 17 views, 10747 downloads, 0 comments
last edited by cong - Jan 7, 2016, 02:25 CET Rating
- Summary:
Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011
- Data Shape: 47 attributes, 4418 instances ()
- License: CC0
- Tags: bioinformatics mhc UCB
- Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
- Download: HDF5 (1.6 MB) XML CSV ARFF LibSVM Matlab Octave
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011
Disclaimer
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.