View Protein Fold Prediction ucsd-mkl (public)

2011-02-28 14:57 by hzahn | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
Rating
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Overall (based on 0 votes)
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Interesting
Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star Documentation
Summary

multy kernel learning dataset on protein fold prediction

License
unknown (from UCI repository)
Dependencies
Tags
multi-class multi-kernel protein-fold-prediction
Attribute Types
Download
unknown (793.6 KB)

Files are converted on demand and the process can take up to a minute. Please wait until download begins.

Completeness of this item currently: 77%.
You can edit this item to add more meta information and make use of the site's premium features.
Original Data Format
unknown
Name
Version mldata
Comment
Names
Data (first 10 data points)
    (Zipped) TAR archive ./._DingShenDam, DingShenDam, DingShenDam/._Composition_Test.csv, DingShenDam/Composition_Test.csv, DingShenDam/._Composition_Train.csv, DingShenDam/Composition_Train.csv, DingShenDam/._Hydrophobicity_Test.csv, DingShenDam/Hydrophobicity_Test.csv, DingShenDam/._Hydrophobicity_Train.csv, DingShenDam/Hydrophobicity_Train.csv, DingShenDam/._L14_Test.csv, DingShenDam/L14_Test.csv, DingShenDam/._L14_Train.csv, DingShenDam/L14_Train.csv, DingShenDam/._L1_Test.csv, DingShenDam/L1_Test.csv, DingShenDam/._L1_Train.csv, DingShenDam/L1_Train.csv, DingShenDam/._L30_Test.csv, DingShenDam/L30_Test.csv, DingShenDam/._L30_Train.csv, DingShenDam/L30_Train.csv, DingShenDam/._L4_Test.csv, DingShenDam/L4_Test.csv, DingShenDam/._L4_Train.csv, DingShenDam/L4_Train.csv, DingShenDam/._Polarity_Test.csv, DingShenDam/Polarity_Test.csv, DingShenDam/._Polarity_Train.csv, DingShenDam/Polarity_Train.csv, DingShenDam/._Polarizability_Test.csv, DingShenDam/Polarizability_Test.csv, DingShenDam/._Polarizability_Train.csv, DingShenDam/Polarizability_Train.csv, DingShenDam/._Secondary_Test.csv, DingShenDam/Secondary_Test.csv, DingShenDam/._Secondary_Train.csv, DingShenDam/Secondary_Train.csv, DingShenDam/._SWblosum62_Test.csv, DingShenDam/SWblosum62_Test.csv, DingShenDam/._SWblosum62_Train.csv, DingShenDam/SWblosum62_Train.csv, DingShenDam/._SWpam50_Test.csv, DingShenDam/SWpam50_Test.csv, DingShenDam/._SWpam50_Train.csv, DingShenDam/SWpam50_Train.csv, DingShenDam/._t_Test.csv, DingShenDam/t_Test.csv, DingShenDam/._t_Train.csv, DingShenDam/t_Train.csv, DingShenDam/._Volume_Test.csv, DingShenDam/Volume_Test.csv, DingShenDam/._Volume_Train.csv, DingShenDam/Volume_Train.csv
Description

This dataset is on protein fold prediction (multiclass classification with 27 classes) based on a subset of the PDB-40D SCOP collection. It is an extension of the original dataset by Ding that also includes the pseudo-amino acid compositions proposed by Shen and Chou and the Smith-Waterman String kernels employed in Damoulas and Girolami.

URLs
http://mkl.ucsd.edu/dataset/protein-fold-prediction
Publications
    Data Source
    The file contains *_Train.csv and *_Test.csv files describing the 12 different feature spaces that should be used to construct individual base kernels for MKL. The data is split to independent train and test sets with 311 samples for training and 383 samples for testing. It also includes the labels in t_Test.csv and t_Train.csv files.
    Measurement Details
    Usage Scenario
    revision 1
    by hzahn on 2011-02-28 14:57

    No one has posted any comments yet. Perhaps you would like to be the first?

    Leave a comment

    To post a comment, please sign in.

    This item was downloaded 2038 times and viewed 574 times.

    No Tasks yet on dataset Protein Fold Prediction ucsd-mkl

    Submit a new Task for this Data item

    Data

    Sort by

    Disclaimer

    We are acting in good faith to make datasets submitted for the use of the scientific community available to everybody, but if you are a copyright holder and would like us to remove a dataset please inform us and we will do it as soon as possible.

    Data | Task | Method | Challenge

    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
    PASCAL Logo
    http://www.pascal-network.org/.