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splice

splice

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Author: Genbank Donor: G. Towell, M. Noordewier, and J. Shavlik Source: [Genbank 64.1](genbank.bio.net) - 1/1/92 Please cite: Primate splice-junction gene sequences (DNA) with associated imperfect domain theory. All examples taken from Genbank 64.1. Categories "ei" and "ie" include every "split-gene" for primates in Genbank 64.1. Non-splice examples taken from sequences known not to include a splicing site. Problem Description: Splice junctions are points on a DNA sequence at which 'superfluous' DNA is removed during the process of protein creation in higher organisms. The problem posed in this dataset is to recognize, given a sequence of DNA, the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out). This problem consists of two subtasks: recognizing exon/intron boundaries (referred to as EI sites), and recognizing intron/exon boundaries (IE sites). (In the biological community, IE borders are referred to a ''acceptors'' while EI borders are referred to as ''donors''.) This dataset has been developed to help evaluate a "hybrid" learning algorithm (KBANN) that uses examples to inductively refine preexisting knowledge. Attributes: > 1 One of {n ei ie}, indicating the class. 2 The instance name. 3-62 The remaining 60 fields are the sequence, starting at position -30 and ending at position +30. Each of these fields is almost always filled by one of {a, g, t, c}. Other characters indicate ambiguity among the standard characters according to the following table: character: meaning D: A or G or T N: A or G or C or T S: C or G R: A or G Notes: * Instance_name is an identifier and should be ignored for modelling

61 features

Class (target)nominal3 unique values
0 missing
Instance_name (ignore)nominal3178 unique values
0 missing
attribute_1nominal5 unique values
0 missing
attribute_2nominal5 unique values
0 missing
attribute_3nominal4 unique values
0 missing
attribute_4nominal4 unique values
0 missing
attribute_5nominal4 unique values
0 missing
attribute_6nominal4 unique values
0 missing
attribute_7nominal4 unique values
0 missing
attribute_8nominal4 unique values
0 missing
attribute_9nominal4 unique values
0 missing
attribute_10nominal4 unique values
0 missing
attribute_11nominal4 unique values
0 missing
attribute_12nominal4 unique values
0 missing
attribute_13nominal4 unique values
0 missing
attribute_14nominal5 unique values
0 missing
attribute_15nominal4 unique values
0 missing
attribute_16nominal4 unique values
0 missing
attribute_17nominal4 unique values
0 missing
attribute_18nominal4 unique values
0 missing
attribute_19nominal5 unique values
0 missing
attribute_20nominal5 unique values
0 missing
attribute_21nominal5 unique values
0 missing
attribute_22nominal5 unique values
0 missing
attribute_23nominal5 unique values
0 missing
attribute_24nominal5 unique values
0 missing
attribute_25nominal5 unique values
0 missing
attribute_26nominal5 unique values
0 missing
attribute_27nominal5 unique values
0 missing
attribute_28nominal5 unique values
0 missing
attribute_29nominal5 unique values
0 missing
attribute_30nominal5 unique values
0 missing
attribute_31nominal5 unique values
0 missing
attribute_32nominal5 unique values
0 missing
attribute_33nominal5 unique values
0 missing
attribute_34nominal5 unique values
0 missing
attribute_35nominal6 unique values
0 missing
attribute_36nominal6 unique values
0 missing
attribute_37nominal5 unique values
0 missing
attribute_38nominal5 unique values
0 missing
attribute_39nominal5 unique values
0 missing
attribute_40nominal5 unique values
0 missing
attribute_41nominal5 unique values
0 missing
attribute_42nominal5 unique values
0 missing
attribute_43nominal5 unique values
0 missing
attribute_44nominal5 unique values
0 missing
attribute_45nominal5 unique values
0 missing
attribute_46nominal5 unique values
0 missing
attribute_47nominal5 unique values
0 missing
attribute_48nominal5 unique values
0 missing
attribute_49nominal5 unique values
0 missing
attribute_50nominal5 unique values
0 missing
attribute_51nominal5 unique values
0 missing
attribute_52nominal5 unique values
0 missing
attribute_53nominal5 unique values
0 missing
attribute_54nominal5 unique values
0 missing
attribute_55nominal5 unique values
0 missing
attribute_56nominal5 unique values
0 missing
attribute_57nominal5 unique values
0 missing
attribute_58nominal5 unique values
0 missing
attribute_59nominal5 unique values
0 missing
attribute_60nominal5 unique values
0 missing

107 properties

3190
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
3
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.
0
Number of numeric attributes.
61
Number of nominal attributes.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
34.16
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.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.9
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
0.54
Standard deviation of the number of distinct values among attributes of the nominal type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.75
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
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
0.27
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.88
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
2
Second quartile (Median) of entropy among attributes.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.9
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
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1655
Number of instances belonging to the most frequent class.
1.67
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.48
Entropy of the target attribute values.
2.01
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.39
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.77
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.
6
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2
Third quartile of entropy among attributes.
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
26.31
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
24.04
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.96
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.98
Average entropy of the attributes.
767
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.06
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.99
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.9
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
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.96
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.06
Average mutual information between the nominal attributes and the target attribute.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.06
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

14 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: attribute_28
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: attribute_4
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: attribute_8
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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