View Lung Cancer (Michigan) (public)

2011-09-14 14:35 by kidzik | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

86 primary lung adenocarcinomas samples and 10 non-neoplastic lung samples are included. Each sample is described by 7129 genes.

License
unknown (from UCI repository)
Dependencies
Tags
cancer lung michigan tumor
Attribute Types
Integer,Floating Point,String
Download
# Instances: 96 / # Attributes: 7130
HDF5 (6.3 MB) XML CSV ARFF LibSVM Matlab Octave

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Completeness of this item currently: 77%.
Original Data Format
h5
Name
'changed
Version mldata
0
Comment
Names
A28102_at,AB000114_at,AB000115_at,AB000220_at,AB000381_s_at,AB000409_at,AB000410_s_at,AB000449_at,AB000450_at,AB000460_at,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
  8. numeric
  9. numeric
  10. numeric
Data (first 10 data points)
    A281... AB00... AB00... AB00... AB00... AB00... AB00... AB00... AB00... AB00... ...
    170.0 69 250 957 25 471.2 -52.0 42.8 200 846.4 ...
    59.7 18 146 186 -7 309.0 -99.0 57.9 151 722.8 ...
    80.0 26 150 340 -16 225.7 23.5 69.4 207 515.1 ...
    92.4 96 177 515 18 296.6 48.5 60.4 151 819.1 ...
    104.0 72 228 540 26 264.1 -10.0 56.4 145 674.4 ...
    88.0 138 115 616 9 371.9 49.2 37.2 149 618.9 ...
    69.7 11 177 380 21 291.0 -62.5 99.0 238 936.2 ...
    230.0 176 511 523 32 664.2 -17.1 295.0 607 1388.1 ...
    105.0 78 233 602 24 471.6 20.0 78.1 300 732.1 ...
    53.7 36 393 160 27 407.3 -4.4 94.2 411 959.1 ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

86 primary lung adenocarcinomas samples and 10 non-neoplastic lung samples are included. Each sample is described by 7129 genes.

URLs
http://datam.i2r.a-star.edu.sg/datasets/krbd/LungCancer/LungCancer-Michigan.html
Publications
    Data Source
    http://datam.i2r.a-star.edu.sg/datasets/krbd/LungCancer/LungCancer-Michigan.html
    Measurement Details
    Usage Scenario
    revision 1
    by kidzik on 2011-09-14 14:35

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    Acknowledgements

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