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AP_Omentum_Kidney

AP_Omentum_Kidney

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Author: Source: Unknown - Date unknown Please cite: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC curve, etc.) for classification, feature selection or clustering algorithms. This repository was inspired by an increasing need in machine learning / bioinformatics communities for a collection of microarray classification problems that could be used by different researches. This way many different classification or feature selection techniques can finally be compared to eachother on the same set of problems. Origin of data Each gene expression sample in GEMLeR repository comes from a large publicly available expO (Expression Project For Oncology) repository by International Genomics Consortium. The goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. The availability of this information translates into direct benefits for patients, researchers and pharma alike. Source: expO website Although there are various other sources of gene expression data available, a decision to use data from expO repository was made because of: - consistency of tissue samples processing procedure - same microarray platform used for all samples - availability of additional information for combined genotype-phenotype studies - availability of a large number of samples for different tumor types In case of publishing material based on GEMLeR datasets, then, please note the assistance you received by using this repository. This will help others to obtain the same datasets and replicate your experiments. Please cite as follows when referring to this repository: Stiglic, G., & Kokol, P. (2010). Stability of Ranked Gene Lists in Large Microarray Analysis Studies. Journal of biomedicine biotechnology, 2010, 616358. You are also welcome to acknowledge the contribution of expO (Expression Project For Oncology) and International Genomics Consortium for providing their gene expression samples to the public.

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Show first 100 features

107 properties

NumberOfInstances
337
Number of instances (rows) of the dataset.
NumberOfFeatures
10936
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
10935
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.94
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.94
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
CfsSubsetEval_DecisionStumpErrRate
0.03
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
CfsSubsetEval_DecisionStumpKappa
0.91
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
CfsSubsetEval_NaiveBayesAUC
0.94
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
CfsSubsetEval_NaiveBayesErrRate
0.03
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
CfsSubsetEval_NaiveBayesKappa
0.91
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
CfsSubsetEval_kNN1NAUC
0.94
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
CfsSubsetEval_kNN1NErrRate
0.03
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
CfsSubsetEval_kNN1NKappa
0.91
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
ClassEntropy
0.78
Entropy of the target attribute values.
DecisionStumpAUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
32.45
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
77.15
Percentage of instances belonging to the most frequent class.
MajorityClassSize
260
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
336.74
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
128312.56
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
18.35
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
65669.6
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
23.59
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
3328.11
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.02
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1816.33
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.13
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
44.43
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.44
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
421.37
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
22.85
Percentage of instances belonging to the least frequent class.
MinorityClassSize
77
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
0.01
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.99
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.01
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
2.83
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
916.61
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.27
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
576.24
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
6.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1510.88
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
2.03
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
862.11
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
19.63
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
2741.82
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.55
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1643.72
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on AP_Omentum_Kidney
76 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
Supervised Classification on AP_Omentum_Kidney
1 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
Supervised Classification on AP_Omentum_Kidney
0 runs - estimation_procedure: 33% Holdout set - target_feature: Tissue
Supervised Data Stream Classification on AP_Omentum_Kidney
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Tissue
Clustering on AP_Omentum_Kidney
0 runs
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Omentum_Kidney
0 runs - estimation_procedure: 50 times Clustering
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