Data
lungcancer_shedden

lungcancer_shedden

active ARFF Publicly available Visibility: public Uploaded 19-02-2015 by Dominik Kirchhoff
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Author: Kerby Shedden et al. Michel Lang Source: Unknown - Date unknown Please cite: Shedden, K., Taylor, J. M. G., Enkemann, S. A., Tsao, M. S., Yeatman, T. J., Gerald, W. L., … Sharma, A. (2008). Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study: Director’s Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma. Nature Medicine, 14(8), 822–827. doi:10.1038/nm.1790 fRMA-normalized. Only "Kratz-genes"*. \* (see: A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies Kratz, Johannes R et al. The Lancet , Volume 379 , Issue 9818 , 823 - 832)

24 features

OS_years (target)numeric332 unique values
0 missing
OS_eventnominal2 unique values
0 missing
histologynominal1 unique values
0 missing
agenumeric50 unique values
0 missing
sexnominal2 unique values
0 missing
g_202387_atnumeric442 unique values
0 missing
g_211475_s_atnumeric442 unique values
0 missing
g_204531_s_atnumeric442 unique values
0 missing
g_211851_x_atnumeric442 unique values
0 missing
g_203967_atnumeric442 unique values
0 missing
g_203968_s_atnumeric442 unique values
0 missing
g_201938_atnumeric442 unique values
0 missing
g_202454_s_atnumeric442 unique values
0 missing
g_215638_atnumeric442 unique values
0 missing
g_214088_s_atnumeric442 unique values
0 missing
g_216010_x_atnumeric442 unique values
0 missing
g_206924_atnumeric442 unique values
0 missing
g_206926_s_atnumeric442 unique values
0 missing
g_204890_s_atnumeric442 unique values
0 missing
g_204891_s_atnumeric442 unique values
0 missing
g_212724_atnumeric442 unique values
0 missing
g_204979_s_atnumeric442 unique values
0 missing
g_AFFX.HUMGAPDH.M33197_5_atnumeric442 unique values
0 missing
g_AFFX.HUMGAPDH.M33197_M_atnumeric442 unique values
0 missing

19 properties

442
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
21
Number of numeric attributes.
3
Number of nominal attributes.
Percentage of instances belonging to the most frequent class.
12.5
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.
8.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
-1.98
Average class difference between consecutive instances.
0
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
87.5
Percentage of numeric attributes.

12 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
3 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: c_index
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