Data
lungcancer_GSE31210

lungcancer_GSE31210

active ARFF Publicly available Visibility: public Uploaded 12-03-2015 by Dominik Kirchhoff
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Author: Okayama et al. Michel Lang Source: Unknown - Date unknown Please cite: Okayama H, Kohno T, Ishii Y, Shimada Y et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 2012 Jan 1;72(1):100-11. 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_event (target)nominal2 unique values
0 missing
OS_yearsnumeric210 unique values
0 missing
histologynominal1 unique values
0 missing
agenumeric35 unique values
0 missing
sexnominal2 unique values
0 missing
g_202387_atnumeric226 unique values
0 missing
g_211475_s_atnumeric226 unique values
0 missing
g_204531_s_atnumeric226 unique values
0 missing
g_211851_x_atnumeric226 unique values
0 missing
g_203967_atnumeric226 unique values
0 missing
g_203968_s_atnumeric226 unique values
0 missing
g_201938_atnumeric226 unique values
0 missing
g_202454_s_atnumeric226 unique values
0 missing
g_215638_atnumeric226 unique values
0 missing
g_214088_s_atnumeric226 unique values
0 missing
g_216010_x_atnumeric226 unique values
0 missing
g_206924_atnumeric226 unique values
0 missing
g_206926_s_atnumeric226 unique values
0 missing
g_204890_s_atnumeric226 unique values
0 missing
g_204891_s_atnumeric226 unique values
0 missing
g_212724_atnumeric226 unique values
0 missing
g_204979_s_atnumeric226 unique values
0 missing
g_AFFX.HUMGAPDH.M33197_5_atnumeric226 unique values
0 missing
g_AFFX.HUMGAPDH.M33197_M_atnumeric226 unique values
0 missing

107 properties

226
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
2
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.
0.5
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.17
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
84.51
Percentage of instances belonging to the most frequent class.
0.95
Mean standard deviation of attributes of the numeric type.
0.79
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.62
Entropy of the target attribute values.
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
191
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
6.21
Second quartile (Median) of means among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1
Maximum entropy among attributes.
-0.49
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
38.77
Maximum kurtosis among attributes of the numeric type.
3.8
Minimum of means among attributes of the numeric type.
0.49
Second quartile (Median) of skewness among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
59.58
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
8.33
Percentage of binary attributes.
0.68
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.11
Number of attributes divided by the number of instances.
0
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
383.18
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.9
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
1.87
Third quartile of kurtosis among attributes of the numeric type.
0.84
Average class difference between consecutive instances.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.53
Maximum skewness among attributes of the numeric type.
0.17
Minimum standard deviation of attributes of the numeric type.
87.5
Percentage of numeric attributes.
9.28
Third quartile of means among attributes of the numeric type.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7.4
Maximum standard deviation of attributes of the numeric type.
15.49
Percentage of instances belonging to the least frequent class.
12.5
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.5
Average entropy of the attributes.
35
Number of instances belonging to the least frequent class.
0
First quartile of entropy among attributes.
1.28
Third quartile of skewness among attributes of the numeric type.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.4
Mean kurtosis among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.04
First quartile of kurtosis among attributes of the numeric type.
0.82
Third quartile of standard deviation of attributes of the numeric type.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
9.38
Mean of means among attributes of the numeric type.
0.23
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.76
First quartile of means among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Average mutual information between the nominal attributes and the target attribute.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
305.97
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.09
First quartile of skewness among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.58
Standard deviation of the number of distinct values among attributes of the nominal type.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.67
Average number of distinct values among the attributes of the nominal type.
0.36
First quartile of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.85
Mean skewness among attributes of the numeric type.

5 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: OS_event
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: c_index
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