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ecoli

ecoli

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Author: Source: Unknown - Please cite: 1. Title: Protein Localization Sites 2. Creator and Maintainer: Kenta Nakai Institue of Molecular and Cellular Biology Osaka, University 1-3 Yamada-oka, Suita 565 Japan nakai@imcb.osaka-u.ac.jp http://www.imcb.osaka-u.ac.jp/nakai/psort.html Donor: Paul Horton (paulh@cs.berkeley.edu) Date: September, 1996 See also: yeast database 3. Past Usage. Reference: "A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins", Paul Horton & Kenta Nakai, Intelligent Systems in Molecular Biology, 109-115. St. Louis, USA 1996. Results: 81% for E.coli with an ad hoc structured probability model. Also similar accuracy for Binary Decision Tree and Bayesian Classifier methods applied by the same authors in unpublished results. Predicted Attribute: Localization site of protein. ( non-numeric ). 4. The references below describe a predecessor to this dataset and its development. They also give results (not cross-validated) for classification by a rule-based expert system with that version of the dataset. Reference: "Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa, PROTEINS: Structure, Function, and Genetics 11:95-110, 1991. Reference: "A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa, Genomics 14:897-911, 1992. 5. Number of Instances: 336 for the E.coli dataset and 6. Number of Attributes. for E.coli dataset: 8 ( 7 predictive, 1 name ) 7. Attribute Information. 1. Sequence Name: Accession number for the SWISS-PROT database 2. mcg: McGeoch's method for signal sequence recognition. 3. gvh: von Heijne's method for signal sequence recognition. 4. lip: von Heijne's Signal Peptidase II consensus sequence score. Binary attribute. 5. chg: Presence of charge on N-terminus of predicted lipoproteins. Binary attribute. 6. aac: score of discriminant analysis of the amino acid content of outer membrane and periplasmic proteins. 7. alm1: score of the ALOM membrane spanning region prediction program. 8. alm2: score of ALOM program after excluding putative cleavable signal regions from the sequence. NOTE - the sequence name has been removed 8. Missing Attribute Values: None. 9. Class Distribution. The class is the localization site. Please see Nakai & Kanehisa referenced above for more details. cp (cytoplasm) 143 im (inner membrane without signal sequence) 77 pp (perisplasm) 52 imU (inner membrane, uncleavable signal sequence) 35 om (outer membrane) 20 omL (outer membrane lipoprotein) 5 imL (inner membrane lipoprotein) 2 imS (inner membrane, cleavable signal sequence) 2

8 features

class (target)nominal8 unique values
0 missing
mcgnumeric78 unique values
0 missing
gvhnumeric63 unique values
0 missing
lipnumeric2 unique values
0 missing
chgnumeric2 unique values
0 missing
aacnumeric59 unique values
0 missing
alm1numeric82 unique values
0 missing
alm2numeric77 unique values
0 missing

107 properties

336
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
8
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.
7
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
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
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
8
Average number of distinct values among the attributes of the nominal type.
0.06
First quartile of skewness among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.89
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.61
Mean skewness among attributes of the numeric type.
0.09
First quartile of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.14
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.71
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
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
42.56
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.26
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.19
Entropy of the target attribute values.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
143
Number of instances belonging to the most frequent class.
-1.04
Minimum kurtosis among attributes of the numeric type.
0.5
Second quartile (Median) of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.5
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
336
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.41
Second quartile (Median) of skewness among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.5
Maximum of means among attributes of the numeric type.
8
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.17
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
0.03
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
29.08
Third quartile of kurtosis among attributes of the numeric type.
0.98
Average class difference between consecutive instances.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
18.33
Maximum skewness among attributes of the numeric type.
0.6
Percentage of instances belonging to the least frequent class.
87.5
Percentage of numeric attributes.
0.5
Third quartile of means among attributes of the numeric type.
0.89
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.22
Maximum standard deviation of attributes of the numeric type.
2
Number of instances belonging to the least frequent class.
12.5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.21
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
5.56
Third quartile of skewness among attributes of the numeric type.
0.71
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
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
51.98
Mean kurtosis among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.94
First quartile of kurtosis among attributes of the numeric type.
0.21
Third quartile of standard deviation of attributes of the numeric type.
0.89
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.5
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.5
First quartile of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

17 tasks

683 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
321 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
320 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
169 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
166 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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